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libobasis24.8-core / opt / libreoffice24.8 / program / libCbcSolver.so.3
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traitsIcESaIcEEE_ZNK21OsiClpSolverInterface11isAbandonedEv_ZNK21OsiClpSolverInterface15isProvenOptimalEv_ZNK21OsiClpSolverInterface24isProvenPrimalInfeasibleEv_ZNK21OsiClpSolverInterface22isProvenDualInfeasibleEv_ZNK21OsiClpSolverInterface29isPrimalObjectiveLimitReachedEv_ZNK21OsiClpSolverInterface27isDualObjectiveLimitReachedEv_ZNK21OsiClpSolverInterface23isIterationLimitReachedEv_ZNK21OsiClpSolverInterface17getEmptyWarmStartEv_ZNK21OsiClpSolverInterface12getWarmStartEv_ZN21OsiClpSolverInterface12setWarmStartEPK13CoinWarmStart_ZN21OsiClpSolverInterface21getPointerToWarmStartERb_ZN21OsiClpSolverInterface12markHotStartEv_ZN21OsiClpSolverInterface17solveFromHotStartEv_ZN21OsiClpSolverInterface14unmarkHotStartEv_ZNK21OsiClpSolverInterface10getRowNameB5cxx11Eij_ZNK21OsiClpSolverInterface10getColNameB5cxx11Eij_ZNK21OsiClpSolverInterface11getRowSenseEv_ZNK21OsiClpSolverInterface16getRightHandSideEv_ZNK21OsiClpSolverInterface11getRowRangeEv_ZNK21OsiClpSolverInterface12isContinuousEi_ZNK21OsiClpSolverInterface8isBinaryEi_ZNK21OsiClpSolverInterface9isIntegerEi_ZNK21OsiClpSolverInterface18isIntegerNonBinaryEi_ZNK21OsiClpSolverInterface12isFreeBinaryEi_ZNK21OsiClpSolverInterface10getColTypeEb_ZNK21OsiClpSolverInterface14getMatrixByRowEv_ZNK21OsiClpSolverInterface14getMatrixByColEv_ZNK21OsiClpSolverInterface21getMutableMatrixByColEv_ZNK21OsiClpSolverInterface14getColSolutionEv_ZNK21OsiClpSolverInterface11getRowPriceEv_ZNK21OsiClpSolverInterface14getReducedCostEv_ZNK21OsiClpSolverInterface14getRowActivityEv_ZNK21OsiClpSolverInterface11getObjValueEv_ZNK21OsiClpSolverInterface11getDualRaysEib_ZNK21OsiClpSolverInterface13getPrimalRaysEi_ZN21OsiClpSolverInterface11setObjCoeffEid_ZN21OsiClpSolverInterface12setColBoundsEidd_ZN21OsiClpSolverInterface15setColSetBoundsEPKiS1_PKd_ZN21OsiClpSolverInterface11setRowLowerEid_ZN21OsiClpSolverInterface11setRowUpperEid_ZN21OsiClpSolverInterface12setRowBoundsEidd_ZN21OsiClpSolverInterface10setRowTypeEicdd_ZN21OsiClpSolverInterface15setRowSetBoundsEPKiS1_PKd_ZN21OsiClpSolverInterface14setRowSetTypesEPKiS1_PKcPKdS5__ZN21OsiClpSolverInterface12setObjectiveEPKd_ZN21OsiClpSolverInterface11setColLowerEPKd_ZN21OsiClpSolverInterface11setColUpperEPKd_ZN21OsiClpSolverInterface10setRowNameEiNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN21OsiClpSolverInterface10setColNameEiNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN21OsiClpSolverInterface13setContinuousEi_ZN21OsiClpSolverInterface10setIntegerEi_ZN21OsiClpSolverInterface13setContinuousEPKii_ZN21OsiClpSolverInterface10setIntegerEPKii_ZN21OsiClpSolverInterface18findIntegersAndSOSEb_ZN21OsiClpSolverInterface11setRowPriceEPKd_ZN21OsiClpSolverInterface6addColERK20CoinPackedVectorBaseddd_ZN21OsiClpSolverInterface6addColERK20CoinPackedVectorBasedddNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN21OsiClpSolverInterface6addColEiPKiPKdddd_ZN21OsiClpSolverInterface6addColEiPKiPKddddNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN21OsiClpSolverInterface7addColsEiPKPK20CoinPackedVectorBasePKdS6_S6__ZN21OsiClpSolverInterface7addColsEiPKiS1_PKdS3_S3_S3__ZN21OsiClpSolverInterface10deleteColsEiPKi_ZN21OsiClpSolverInterface6addRowERK20CoinPackedVectorBasedd_ZN21OsiClpSolverInterface6addRowERK20CoinPackedVectorBaseddNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN21OsiClpSolverInterface6addRowERK20CoinPackedVectorBasecdd_ZN21OsiClpSolverInterface6addRowEiPKiPKddd_ZN21OsiClpSolverInterface6addRowERK20CoinPackedVectorBasecddNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN21OsiClpSolverInterface7addRowsEiPKPK20CoinPackedVectorBasePKdS6__ZN21OsiClpSolverInterface7addRowsEiPKPK20CoinPackedVectorBasePKcPKdS8__ZN21OsiClpSolverInterface7addRowsEiPKiS1_PKdS3_S3__ZN21OsiClpSolverInterface10deleteRowsEiPKi_ZN21OsiClpSolverInterface13saveBaseModelEv_ZN21OsiClpSolverInterface16restoreBaseModelEi_ZN21OsiClpSolverInterface12applyRowCutsEiPK9OsiRowCut_ZN21OsiClpSolverInterface12applyRowCutsEiPPK9OsiRowCut_ZN21OsiClpSolverInterface9applyCutsERK7OsiCutsd_ZN21OsiClpSolverInterface11loadProblemERK16CoinPackedMatrixPKdS4_S4_S4_S4__ZN21OsiClpSolverInterface13assignProblemERP16CoinPackedMatrixRPdS4_S4_S4_S4__ZN21OsiClpSolverInterface11loadProblemERK16CoinPackedMatrixPKdS4_S4_PKcS4_S4__ZN21OsiClpSolverInterface13assignProblemERP16CoinPackedMatrixRPdS4_S4_RPcS4_S4__ZN21OsiClpSolverInterface11loadProblemERK13ClpMatrixBasePKdS4_S4_S4_S4__ZN21OsiClpSolverInterface11loadProblemEiiPKiS1_PKdS3_S3_S3_S3_S3__ZN21OsiClpSolverInterface11loadProblemEiiPKiS1_PKdS3_S3_S3_PKcS3_S3__ZN21OsiClpSolverInterface7readMpsEPKcS1__ZN21OsiClpSolverInterface7readMpsEPKcS1_RiRPP7CoinSet_ZNK21OsiClpSolverInterface8writeMpsEPKcS1_d_ZNK21OsiClpSolverInterface14writeMpsNativeEPKcPS1_S2_iid_ZN21OsiClpSolverInterface6readLpEPKcd_ZNK21OsiClpSolverInterface7writeLpEPKcS1_diidb_ZNK21OsiClpSolverInterface7writeLpEP8_IO_FILEdiidb_ZN21OsiClpSolverInterface21replaceMatrixOptionalERK16CoinPackedMatrix_ZN21OsiClpSolverInterface13replaceMatrixERK16CoinPackedMatrix_ZN21OsiClpSolverInterface20passInMessageHandlerEP18CoinMessageHandler_ZN21OsiClpSolverInterface13tightenBoundsEi_ZNK21OsiClpSolverInterface8getSizeLEv_ZNK21OsiClpSolverInterface8getSizeUEv_ZN21OsiClpSolverInterface16synchronizeModelEv_ZNK21OsiClpSolverInterface5cloneEb_ZN21OsiClpSolverInterface5resetEv_ZN21OsiClpSolverInterface11applyRowCutERK9OsiRowCut_ZN21OsiClpSolverInterface11applyColCutERK9OsiColCut_ZTv0_n24_N21OsiClpSolverInterface12initialSolveEv_ZTv0_n32_N21OsiClpSolverInterface7resolveEv_ZTv0_n40_N21OsiClpSolverInterface14branchAndBoundEv_ZTv0_n48_N21OsiClpSolverInterface11setIntParamE11OsiIntParami_ZTv0_n56_N21OsiClpSolverInterface11setDblParamE11OsiDblParamd_ZTv0_n64_N21OsiClpSolverInterface11setStrParamE11OsiStrParamRKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZTv0_n72_N21OsiClpSolverInterface12setHintParamE12OsiHintParamb15OsiHintStrengthPv_ZTv0_n80_NK21OsiClpSolverInterface11getIntParamE11OsiIntParamRi_ZTv0_n88_NK21OsiClpSolverInterface11getDblParamE11OsiDblParamRd_ZTv0_n96_NK21OsiClpSolverInterface11getStrParamE11OsiStrParamRNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZTv0_n128_NK21OsiClpSolverInterface11isAbandonedEv_ZTv0_n136_NK21OsiClpSolverInterface15isProvenOptimalEv_ZTv0_n144_NK21OsiClpSolverInterface24isProvenPrimalInfeasibleEv_ZTv0_n152_NK21OsiClpSolverInterface22isProvenDualInfeasibleEv_ZTv0_n160_NK21OsiClpSolverInterface29isPrimalObjectiveLimitReachedEv_ZTv0_n168_NK21OsiClpSolverInterface27isDualObjectiveLimitReachedEv_ZTv0_n176_NK21OsiClpSolverInterface23isIterationLimitReachedEv_ZTv0_n184_NK21OsiClpSolverInterface17getEmptyWarmStartEv_ZTv0_n192_NK21OsiClpSolverInterface12getWarmStartEv_ZTv0_n200_N21OsiClpSolverInterface21getPointerToWarmStartERb_ZTv0_n208_N21OsiClpSolverInterface12setWarmStartEPK13CoinWarmStart_ZTv0_n216_N21OsiClpSolverInterface12markHotStartEv_ZTv0_n224_N21OsiClpSolverInterface17solveFromHotStartEv_ZTv0_n232_N21OsiClpSolverInterface14unmarkHotStartEv_ZNK18OsiSolverInterface14getNumIntegersEv_ZTv0_n288_NK21OsiClpSolverInterface11getRowSenseEv_ZTv0_n296_NK21OsiClpSolverInterface16getRightHandSideEv_ZTv0_n304_NK21OsiClpSolverInterface11getRowRangeEv_ZTv0_n344_NK21OsiClpSolverInterface12isContinuousEi_ZTv0_n352_NK21OsiClpSolverInterface8isBinaryEi_ZTv0_n360_NK21OsiClpSolverInterface9isIntegerEi_ZTv0_n368_NK21OsiClpSolverInterface18isIntegerNonBinaryEi_ZTv0_n376_NK21OsiClpSolverInterface12isFreeBinaryEi_ZTv0_n384_NK21OsiClpSolverInterface10getColTypeEb_ZTv0_n392_NK21OsiClpSolverInterface14getMatrixByRowEv_ZTv0_n400_NK21OsiClpSolverInterface14getMatrixByColEv_ZTv0_n416_NK21OsiClpSolverInterface21getMutableMatrixByColEv_ZTv0_n432_NK21OsiClpSolverInterface14getColSolutionEv_ZN18OsiSolverInterface20getStrictColSolutionEv_ZTv0_n448_NK21OsiClpSolverInterface11getRowPriceEv_ZTv0_n456_NK21OsiClpSolverInterface14getReducedCostEv_ZTv0_n464_NK21OsiClpSolverInterface14getRowActivityEv_ZTv0_n472_NK21OsiClpSolverInterface11getObjValueEv_ZTv0_n488_NK21OsiClpSolverInterface11getDualRaysEib_ZTv0_n496_NK21OsiClpSolverInterface13getPrimalRaysEi_ZNK18OsiSolverInterface20getFractionalIndicesEd_ZTv0_n512_N21OsiClpSolverInterface11setObjCoeffEid_ZN18OsiSolverInterface14setObjCoeffSetEPKiS1_PKd_ZTv0_n528_N21OsiClpSolverInterface12setObjectiveEPKd_ZTv0_n544_N21OsiClpSolverInterface11setColLowerEid_ZTv0_n552_N21OsiClpSolverInterface11setColLowerEPKd_ZTv0_n560_N21OsiClpSolverInterface11setColUpperEid_ZTv0_n568_N21OsiClpSolverInterface11setColUpperEPKd_ZTv0_n576_N21OsiClpSolverInterface12setColBoundsEidd_ZTv0_n584_N21OsiClpSolverInterface15setColSetBoundsEPKiS1_PKd_ZTv0_n592_N21OsiClpSolverInterface11setRowLowerEid_ZTv0_n600_N21OsiClpSolverInterface11setRowUpperEid_ZTv0_n608_N21OsiClpSolverInterface12setRowBoundsEidd_ZTv0_n616_N21OsiClpSolverInterface15setRowSetBoundsEPKiS1_PKd_ZTv0_n624_N21OsiClpSolverInterface10setRowTypeEicdd_ZTv0_n632_N21OsiClpSolverInterface14setRowSetTypesEPKiS1_PKcPKdS5__ZTv0_n640_N21OsiClpSolverInterface14setColSolutionEPKd_ZTv0_n648_N21OsiClpSolverInterface11setRowPriceEPKd_ZN18OsiSolverInterface14reducedCostFixEdb_ZTv0_n664_N21OsiClpSolverInterface13setContinuousEi_ZTv0_n672_N21OsiClpSolverInterface10setIntegerEi_ZTv0_n680_N21OsiClpSolverInterface13setContinuousEPKii_ZTv0_n688_N21OsiClpSolverInterface10setIntegerEPKii_ZNK18OsiSolverInterface14dfltRowColNameB5cxx11Ecij_ZNK18OsiSolverInterface10getObjNameB5cxx11Ej_ZTv0_n720_NK21OsiClpSolverInterface10getRowNameB5cxx11Eij_ZN18OsiSolverInterface11getRowNamesB5cxx11Ev_ZTv0_n736_N21OsiClpSolverInterface10setRowNameEiNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN18OsiSolverInterface11setRowNamesERSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS6_EEiii_ZN18OsiSolverInterface14deleteRowNamesEii_ZTv0_n760_NK21OsiClpSolverInterface10getColNameB5cxx11Eij_ZN18OsiSolverInterface11getColNamesB5cxx11Ev_ZTv0_n776_N21OsiClpSolverInterface10setColNameEiNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN18OsiSolverInterface11setColNamesERSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS6_EEiii_ZN18OsiSolverInterface14deleteColNamesEii_ZTv0_n800_N21OsiClpSolverInterface6addColERK20CoinPackedVectorBaseddd_ZTv0_n808_N21OsiClpSolverInterface6addColERK20CoinPackedVectorBasedddNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZTv0_n816_N21OsiClpSolverInterface6addColEiPKiPKdddd_ZTv0_n824_N21OsiClpSolverInterface6addColEiPKiPKddddNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZTv0_n832_N21OsiClpSolverInterface7addColsEiPKPK20CoinPackedVectorBasePKdS6_S6__ZTv0_n840_N21OsiClpSolverInterface7addColsEiPKiS1_PKdS3_S3_S3__ZTv0_n848_N21OsiClpSolverInterface10deleteColsEiPKi_ZTv0_n856_N21OsiClpSolverInterface6addRowERK20CoinPackedVectorBasedd_ZTv0_n864_N21OsiClpSolverInterface6addRowERK20CoinPackedVectorBaseddNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZTv0_n872_N21OsiClpSolverInterface6addRowERK20CoinPackedVectorBasecdd_ZTv0_n880_N21OsiClpSolverInterface6addRowERK20CoinPackedVectorBasecddNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZTv0_n888_N21OsiClpSolverInterface6addRowEiPKiPKddd_ZTv0_n896_N21OsiClpSolverInterface7addRowsEiPKPK20CoinPackedVectorBasePKdS6__ZTv0_n904_N21OsiClpSolverInterface7addRowsEiPKPK20CoinPackedVectorBasePKcPKdS8__ZTv0_n912_N21OsiClpSolverInterface7addRowsEiPKiS1_PKdS3_S3__ZTv0_n920_N21OsiClpSolverInterface10deleteRowsEiPKi_ZTv0_n928_N21OsiClpSolverInterface21replaceMatrixOptionalERK16CoinPackedMatrix_ZTv0_n936_N21OsiClpSolverInterface13replaceMatrixERK16CoinPackedMatrix_ZTv0_n944_N21OsiClpSolverInterface13saveBaseModelEv_ZTv0_n952_N21OsiClpSolverInterface16restoreBaseModelEi_ZTv0_n960_N21OsiClpSolverInterface9applyCutsERK7OsiCutsd_ZTv0_n968_N21OsiClpSolverInterface12applyRowCutsEiPK9OsiRowCut_ZTv0_n976_N21OsiClpSolverInterface12applyRowCutsEiPPK9OsiRowCut_ZTv0_n984_N21OsiClpSolverInterface11loadProblemERK16CoinPackedMatrixPKdS4_S4_S4_S4__ZTv0_n992_N21OsiClpSolverInterface13assignProblemERP16CoinPackedMatrixRPdS4_S4_S4_S4__ZTv0_n1000_N21OsiClpSolverInterface11loadProblemERK16CoinPackedMatrixPKdS4_S4_PKcS4_S4__ZTv0_n1008_N21OsiClpSolverInterface13assignProblemERP16CoinPackedMatrixRPdS4_S4_RPcS4_S4__ZTv0_n1016_N21OsiClpSolverInterface11loadProblemEiiPKiS1_PKdS3_S3_S3_S3_S3__ZTv0_n1024_N21OsiClpSolverInterface11loadProblemEiiPKiS1_PKdS3_S3_S3_PKcS3_S3__ZTv0_n1032_N21OsiClpSolverInterface17loadFromCoinModelER9CoinModelb_ZTv0_n1040_N21OsiClpSolverInterface7readMpsEPKcS1__ZTv0_n1048_N21OsiClpSolverInterface7readMpsEPKcS1_RiRPP7CoinSet_ZN18OsiSolverInterface8readGMPLEPKcS1__ZTv0_n1064_NK21OsiClpSolverInterface8writeMpsEPKcS1_d_ZTv0_n1072_NK21OsiClpSolverInterface7writeLpEPKcS1_diidb_ZTv0_n1080_NK21OsiClpSolverInterface7writeLpEP8_IO_FILEdiidb_ZTv0_n1088_N21OsiClpSolverInterface6readLpEPKcd_ZTv0_n1096_N21OsiClpSolverInterface20passInMessageHandlerEP18CoinMessageHandler_ZTv0_n1104_N21OsiClpSolverInterface18findIntegersAndSOSEb_ZN18OsiSolverInterface22activateRowCutDebuggerEPKc_ZN18OsiSolverInterface22activateRowCutDebuggerEPKdb_ZTv0_n1128_NK21OsiClpSolverInterface21canDoSimplexInterfaceEv_ZTv0_n1136_NK21OsiClpSolverInterface19enableFactorizationEv_ZTv0_n1144_NK21OsiClpSolverInterface20disableFactorizationEv_ZTv0_n1152_NK21OsiClpSolverInterface16basisIsAvailableEv_ZTv0_n1160_NK21OsiClpSolverInterface14getBasisStatusEPiS0__ZTv0_n1168_N21OsiClpSolverInterface14setBasisStatusEPKiS1__ZTv0_n1176_NK21OsiClpSolverInterface18getReducedGradientEPdS0_PKd_ZTv0_n1184_NK21OsiClpSolverInterface11getBInvARowEiPdS0__ZTv0_n1192_NK21OsiClpSolverInterface10getBInvRowEiPd_ZTv0_n1200_NK21OsiClpSolverInterface11getBInvAColEiPd_ZTv0_n1208_NK21OsiClpSolverInterface10getBInvColEiPd_ZTv0_n1216_NK21OsiClpSolverInterface9getBasicsEPi_ZTv0_n1224_N21OsiClpSolverInterface22enableSimplexInterfaceEb_ZTv0_n1232_N21OsiClpSolverInterface23disableSimplexInterfaceEv_ZTv0_n1240_N21OsiClpSolverInterface5pivotEiii_ZTv0_n1248_N21OsiClpSolverInterface17primalPivotResultEiiRiS0_RdP16CoinPackedVector_ZTv0_n1256_N21OsiClpSolverInterface15dualPivotResultERiS0_iiRdP16CoinPackedVector_ZTv0_n1264_NK21OsiClpSolverInterface5cloneEb_ZTv0_n1280_N21OsiClpSolverInterface5resetEv_ZTv0_n1288_N21OsiClpSolverInterface11applyRowCutERK9OsiRowCut_ZTv0_n1296_N21OsiClpSolverInterface11applyColCutERK9OsiColCut_ZNK12CbcOsiSolver5cloneEb_ZTv0_n1264_NK12CbcOsiSolver5cloneEb_ZN12CbcHeuristic13shouldHeurRunEi_ZNK9OsiObject18checkInfeasibilityEPK23OsiBranchingInformation_ZNK9OsiObject14feasibleRegionEP18OsiSolverInterface_ZNK9OsiObject12columnNumberEv_ZNK6OsiSOS10upEstimateEv_ZNK6OsiSOS12downEstimateEv_ZNK9OsiObject10upEstimateEv_ZNK9OsiObject12downEstimateEv_ZNK16OsiSimpleInteger14feasibleRegionEP18OsiSolverInterfacePK23OsiBranchingInformation_ZNK16OsiSimpleInteger12columnNumberEv_ZNK16OsiSimpleInteger10upEstimateEv_ZNK16OsiSimpleInteger12downEstimateEv_ZN16OsiSimpleInteger11resetBoundsEPK18OsiSolverInterface_ZN16OsiSimpleInteger16resetSequenceEtcEiPKi_ZNK15CglCutGenerator24mayGenerateRowCutsInTreeEv_ZNK15CglCutGenerator17needsOptimalBasisEv_ZN17OsiChooseVariable16feasibleSolutionEPK23OsiBranchingInformationPKdiPPK9OsiObject_ZN17OsiChooseVar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IDHcIÀM9Âÿÿÿµ êÿÿIL¸éÿÿE1ÉL÷ÿµéÿÿHÀéÿÿH¨éÿÿjÿ0I_L÷AXÿØHêÿÿHÐéÿÿéÿÿ½éÿÿ8êÿÿÁ9ø#AD$ÊH)úHxêÿÿIcÄHTH÷ØHêÿÿHÀéÿÿHH êÿÿH¸éÿÿHÂHêÿÿGÿD)Ʌøéÿÿë	fHxêÿÿøéÿÿxêÿÿµ8êÿÿò0êÿÿD$IHöxòX@êÿÿDæL÷ÿÐIDæL÷ÿ¿@ètõÿDâLöHÇHHêÿÿèϙõÿHxêÿÿHHêÿÿH(êÿÿò@êÿÿÇ@@BHDÊøH êÿÿDdüHêÿÿòTÈøHAH9êÿÿ<ÿÿÿLc¥xêÿÿéÿÿéÿÿI)щxêÿÿ΋8êÿÿH`É8êÿÿHcÖfïÉL÷LÀéÿÿHµèéÿÿAHÐéÿÿòÎH¸éÿÿrfTÆfWÆòÑf(ÁLÒÿпHèwõÿxêÿÿE1ÀLöHÇHÀéÿÿA¹HxêÿÿèoõÿHxêÿÿÐéÿÿE H½(êÿÿAHcéÿÿÆA@HǍ@éÿÿHêÿÿHÐéÿÿHÐéÿÿ9ÓéÿÿéÿÿéÑûÿÿE1äH½pêÿÿèOõÿH½`êÿÿèÕõÿLÿ軕õÿé¿öÿÿHêÿÿÇPêÿÿ1öH=b;1Àè£zõÿHcPêÿÿHÀéÿÿHÁH¸þÿÿÿÿÿÿH9ÈHñÿÿéðöÿf.ò8Åò@êÿÿò0êÿÿéøöÿÿò@êÿÿH¸éÿÿL÷HÀéÿÿf(ÁÿÐéÿÿÿH(êÿÿDæL÷è*õÿEä~;H(êÿÿHxêÿÿN$áHxêÿÿH8HÿtHÿPHxêÿÿHxêÿÿL9àu×H½(êÿÿèîõÿHíÿÿIÄHÀIIL÷ÿpHH L÷HêÿÿHH(H êÿÿHH0H@8H(êÿÿH0êÿÿIÿ HHêÿÿIL÷ÿ(PêÿÿH@êÿÿÒÈH éÿÿLàL¥xêÿÿ1öIÜHÁHPêÿÿë#HxêÿÿHPêÿÿIÄHxêÿÿH9ÈHxêÿÿÀtÐIc$ÀxÈHðéÿÿH½êÿÿN8êÿÿ²H(êÿÿHcH×H½ êÿÿHH½0êÿÿ4H½HêÿÿòÇH½@êÿÿòÇH½éÿÿèӒõÿHcµ8êÿÿéZÿÿÿ1öIHðéÿÿL÷ÿHêÿÿH°éÿÿL÷HcL$IÿØA$H½éÿÿèaõÿH½¸éÿÿèUõÿH½¨éÿÿèIõÿH½Àéÿÿè=õÿIfïÀL÷H0øH5´8ÿA¼é	ýÿÿ1Àé¶öÿÿǅ8êÿÿE1äǅHêÿÿé´÷ÿÿHÂDæL÷èӋõÿéäýÿÿHÃé؏öÿHÃéöÿHÃéñöÿHÃéöÿHÃé"öÿIÆé5öÿHÃéöÿHÃéþöÿé5öÿHÃ鰏öÿHÃé-öÿHÃé1öÿé׏öÿHÃé.öÿHÃé?öÿHÃé؏öÿHÃéЏöÿHÃéJöÿHÃéޏöÿHÃé+öÿé0öÿHÃé>öÿUHåAWAVL½°üÿÿIöHþAULÿATSLËHìxHxüÿÿLmH`üÿÿDüÿÿè}õÿLc¥¼üÿÿEäsǅüÿÿµüÿÿLÿ貏õÿH
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ÉXýÿÿémöÿÉKýÿÿE1äésýÿÿéXöÿéXöÿóúHìHÄÃ2.9.10%s begin
%s start[%i] = %i
%s return
ROW%5i%6d %8s %13gCOL%5i %13ibasic_string::appendCbc_C_Interface-solve-quit%s ERROR: %s::%s, %s
basic_string: construction from null is not valid%s numcols = %i, numrows = %i, numelem = %i
%s model = %p, start = %p, index = %p, value = %p
%s index[%i] = %i, value[%i] = %g
%s collb = %p, colub = %p, obj = %p, rowlb = %p, rowub = %p
%s optimization direction = %g
  (1 - minimize, -1 - maximize, 0 - ignore)%s collb[%i] = %g, colub[%i] = %g, obj[%i] = %g
%s rowlb[%i] = %g, rowub[%i] = %g
--------------------------------------                       Primal          Lower         Upper                       Primal          Lower         Upper          Cost     isIntegervector::_M_realloc_insertû¬öÿö¬öÿï¬öÿè¬öÿá¬öÿڬöÿӬöÿ̬öÿ9CoinError18Cbc_MessageHandlerCbc_C_Interface::Cbc_printModel(::Cbc_solve(): :0âyE>:0âyE¾-CBC_CLP_ENVIRONMENTbasic_string::substrplusPLUSminusMINUSEOL<Possible options for ;
	current  >directoryCurrent working directory is printM was provided for  - valid range is  to %s was changed from %g to %g%s was changed from %d to %dOption for  changed from minus%dplus%dSwitching to line modeimportString of rbError in freadUnable to open file _fix_read_wbError in fwriteFor help???From stdinnoallC!ommandsmoreallow!ableGapImport basis from bas filebasisO!utbiasLUoff1nativefudge!Long_dummyMumps_dummyclique!CutsforceOnvariablecolumn!Order?so!low_halimidiot3idiot7cross!overcutL!engthWhether to try decompositiondense!Thresholddextra4Minimize or MaximizedirNetlibDiving solve optionDivingC!oefficientbothDivingG!uidedDivingP!seudoCostSolves dual reformulationDual pivot choice algorithmsteep!esteither!Simplexexpensive!Strongextra2denseFraction in feasibility pumpWhether to regularize barrierdeltalonglongendonlygreedy!HeuristicHeuristic optionshot!StartMaxItsImport model from mps fileinc!rementforceandgloballagomory!Cutsbothinsteadendonlyrootendcleanbothaswelllocal!TreeSearchmax!imizemaxF!actormaxIt!erationsMaximum number of nodes to domaxN!odesmaxSaved!SolutionsNumber of solutions to save.maxSo!lutionsmin!imizeDubious options for mipmipO!ptionsMore dubious options for mipmore!MipOptionsmore2!MipOptionsmixed!IntegerRoundingCutsifmovemess!agesDo some of miplib test setmiplibmips!tartmoreS!pecialOptionsmoreT!unemultiple!RootPassesnaive!HeuristicsSolve entire netlib test setnetlibnetlibB!arriernetlibD!ualnetlibP!rimalnetlibT!uneTries to make network matrixnetworknextB!estSolutionhybridnode!StrategyupfewestdownfewestupdepthdowndepthNumber of analysis iterationsnumberA!nalyzeobjective!ScaleoutDup!licatesWhich output format to useoutput!Formatpara!metricspassC!utspassF!easibilityPumpHow many passes in presolvepassP!resolveNumber of cut passes in treepassT!reeCutsThe default is one passMethod of perturbationpertV!alueWhether to perturb problemperturb!ationPFIpivotAndC!omplementpivotAndF!ixTries to make +- 1 matrixplus!MinusDubious print optionsPresolve optionspreO!ptWhether to presolve problempreprocesssaveequaltrysosequalallstrategyaggregateforcesosTolerance to use in presolvepreT!oleranceauto!maticPrimal pivot choice algorithmprimalP!ivotexa!ctdant!zigpart!ialchangesprintDo primal simplex algorithmprimalS!implexprimalT!oleranceprimalW!eightnormalPrint optionsprinti!ngOptionsintegerspecialrowsbound!rangingrhs!rangingobjective!rangingstatsboundsintboundsallprintM!askprio!rityInWhether to use Probing cutsprobing!CutsforceonglobalforceOnButforceOnStrongforceOnButStrongstrongRootproximity!Search100300pumpC!utoffpumpI!ncrementpumpT!uneStops clp executionRandom seed for CbcrandomC!bcSeedrandomi!zedRoundingRandom seed for ClprandomS!eedratio!Gapreads solution from filerestoreS!olutionImport stored cuts from filereadSt!oredreallyO!bjectiveScaleScales model in placereallyS!calereduce!AndSplitCutsreduce2!AndSplitCutslongOnlongRootresidual!CapacityCutsrestore!ModelReverses sign of objectivereverserhs!ScaleRens200100010000djbeforeusesolutionRinsoftenround!ingHeuristicSave model to binary filesaveM!odelsaves solution to filesaveS!olutionWhether to scale problemscal!ingequi!libriumgeo!metricdynamicrows!onlymaximum secondssec!ondsfor debugsleepslow!cutpassesslp!Valuesmall!FactorizationPrints solution to filesolu!tionSolve problemsolv!eWhether to use SOS from AMPLsos!Optionsslog!Levelspars!eFactorspecial!OptionsWhether to try sprint crashsprint!CrashPrint some statisticsstat!isticsstopstrat!egystrong!Branchingsubs!titutionTest OsiObject stufftestO!sitighten!FactortightLPcputimeM!odeelapsedtrust!PseudoCostsDubious tuning parameterstune!PreProcesstwo!MirCutsforceLongOnHand coded Clp stuffuserCbcintreevectorverbosezeroT!olerance%s: __pos (which is %zu) > this->size() (which is %zu)Current value of printMask is Current default (if $ as parameter) for <Range of values is %g to %g;
	current %g>
<Range of values is %d to %d;
	current %d>
%g was provided for %s - valid range is %g to %g%d was provided for %s - valid range is %d to %dusing time of day %s was changed from %d to %dOption for %s changed from %s to %sOption for %s given illegal value %sMismatch on rows and/or columns - giving upMismatch on rows and/or columns - truncating%d value of %g - bounds %g %g
Whether to print less used commandsFor the sake of your sanity, only the more useful and simple commands are printed out on ?.Stop when gap between best possible and best less than thisMainly useful for tuning purposes.  Normally the first dual or primal will be using an all slack basis anyway.If you think you may get odd objective values or large equality rows etc then it may be worth setting this true.  It is still experimental and you may prefer to use objective!Scale and rhs!Scale.This will read an MPS format basis file from the given file name.  It will use the default directory given by 'directory'.  A name of '$' will use the previous value for the name.  This is initialized to '', i.e. it must be set.  If you have libz then it can read compressed files 'xxxxxxxx.gz' or xxxxxxxx.bz2.This does branch and cut.  There are many parameters which can affect the performance.  First just try with default settings and look carefully at the log file.  Did cuts help?  Did they take too long?  Look at output to see which cuts were effective and then do some tuning.  You will see that the options for cuts are off, on, root and ifmove, forceon.  Off is obvious, on means that this cut generator will be tried in the branch and cut tree (you can fine tune using 'depth').  Root means just at the root node while 'ifmove' means that cuts will be used in the tree if they look as if they are doing some good and moving the objective value.  Forceon is same as on but forces code to use cut generator at every node.  For probing forceonbut just does fixing probing in tree - not strengthening etc.  If pre-processing reduced the size of the problem or strengthened many coefficients then it is probably wise to leave it on.  Switch off heuristics which did not provide solutions.  The other major area to look at is the search.  Hopefully good solutions were obtained fairly early in the search so the important point is to select the best variable to branch on.  See whether strong branching did a good job - or did it just take a lot of iterations.  Adjust the strongBranching and trustPseudoCosts parameters.  If cuts did a good job, then you may wish to have more rounds of cuts - see passC!uts and passT!ree.For a barrier code to be effective it needs a good Cholesky ordering and factorization.  The native ordering and factorization is not state of the art, although acceptable.  You may want to link in one from another source.  See Makefile.locations for some possibilities.This switches on a heuristic which does branch and cut on the problem given by just using variables which have appeared in one or more solutions. It obviously only tries after two or more solutions. See Rounding for meaning of on,both,before If the user has Cplex, but wants to use some of Cbc's heuristics then you can!  If this is on, then Cbc will get to the root node and then hand over to Cplex.  If heuristics find a solution this can be significantly quicker.  You will probably want to switch off Cbc's cuts as Cplex thinks they are genuine constraints.  It is also probable that you want to switch off preprocessing, although for difficult problems it is worth trying both.Once you like what the stand-alone solver does then this allows you to generate user_driver.cpp which approximates the code.  0 gives simplest driver, 1 generates saves and restores, 2 generates saves and restores even for variables at default value. 4 bit in cbc generates size dependent code rather than computed values.  This is now deprecated as you can call stand-alone solver - see Cbc/examples/driver4.cpp.If crash is set on and there is an all slack basis then Clp will flip or put structural variables into basis with the aim of getting dual feasible.  On the whole dual seems to be better without it and there are alternative types of 'crash' for primal e.g. 'idiot' or 'sprint'. I have also added a variant due to Solow and Halim which is as on but just flip.Interior point algorithms do not obtain a basic solution (and the feasibility criterion is a bit suspect (JJF)).  This option will crossover to a basic solution suitable for ranging or branch and cut.  With the current state of quadratic it may be a good idea to switch off crossover for quadratic (and maybe presolve as well) - the option maybe does this.Depth in tree at which to do cutsCut generators may be - off, on only at root, on if they look possible and on.  If they are done every node then that is that, but it may be worth doing them every so often.  The original method was every so many nodes but it is more logical to do it whenever depth in tree is a multiple of K.  This option does that and defaults to -1 (off -> code decides).At present this only applies to Gomory cuts. -1 (default) leaves as is. Any value >0 says that all cuts <= this length can be generated both at root node and in tree. 0 says to use some dynamic lengths.  If value >=10,000,000 then the length in tree is value%10000000 - so 10000100 means unlimited length at root and 100 in tree.All solutions must be better than this value (in a minimization sense).  This is also set by code whenever it obtains a solution and is set to value of objective for solution minus cutoff increment.This can be used to switch on or off all cuts (apart from Reduce and Split).  Then you can do individual ones off or on See branchAndCut for information on options.This will read a solution file from the given file name.  It will use the default directory given by 'directory'.  A name of '$' will use the previous value for the name.  This is initialized to '', i.e. it must be set.

If set to create it will create a file called debug.file  after search.

The idea is that if you suspect a bad cut generator you can do a good run with debug set to 'create' and then switch on the cuts you suspect and re-run with debug set to 'debug.file'  The create case has same effect as saveSolution.Rather a complicated parameter but can be useful. -1 means off for large problems but on as if -12 for problems where rows+columns<500, -2 means use Cplex if it is linked in.  Otherwise if negative then go into depth first complete search fast branch and bound when depth>= -value-2 (so -3 will use this at depth>=1).  This mode is only switched on after 500 nodes.  If you really want to switch it off for small problems then set this to -999.  If >=0 the value doesn't matter very much.  The code will do approximately 100 nodes of fast branch and bound every now and then at depth>=5.  The actual logic is too twisted to describe here.Whether to try Distance Induced Neighborhood SearchThe default is minimize - use 'direction maximize' for maximization.
You can also use the parameters 'maximize' or 'minimize'.Set directory where the COIN-OR sample problems are.This sets the directory where the netlib problems reside. One can get the netlib problems from COIN-OR or from the main netlib site. This parameter is used only when -netlib is passed to clp. clp will pick up the netlib problems from this directory. If clp is built without zlib support then the problems must be uncompressed. It is initialized to '../../Data/Netlib'This sets the directory where the miplib 2003 problems reside. One can get the miplib problems from COIN-OR or from the main miplib site. This parameter is used only when -miplib is passed to cbc. cbc will pick up the miplib problems from this directory. If cbc is built without zlib support then the problems must be uncompressed. It is initialized to '../../Data/miplib3'If >2 && <20 then modify diving options - 	 
	3 only at root and if no solution,  	 
	4 only at root and if this heuristic has not got solution, 	 
	5 decay only if no solution, 	 
	6 if depth <3 or decay, 	 
	7 run up to 2 times if solution found 4 otherwise, 	 
	>10 All only at root (DivingC normal as value-10), 	 
	>20 All with value-20).If >0 then do up to this many solves.  Last digit is ignored and used for extra options - 	 
	1-3 allow fixing of satisfied integers (but not at bound) 	 
	1 switch off above for that dive if goes infeasible 	 
	2 switch off above permanently if goes infeasibleWhether to try Diving heuristicsNormally heuristics are done in branch and bound.  It may be useful to do them outside. Only those heuristics with 'both' or 'before' set will run.  Doing this may also set cutoff, which can help with preprocessing.The dual algorithm in Clp is a single phase algorithm as opposed to a two phase algorithm where you first get feasible then optimal.  If a problem has both upper and lower bounds then it is trivial to get dual feasible by setting non basic variables to correct bound.  If the gap between the upper and lower bounds of a variable is more than the value of dualBound Clp introduces fake bounds so that it can make the problem dual feasible.  This has the same effect as a composite objective function in the primal algorithm.  Too high a value may mean more iterations, while too low a bound means the code may go all the way and then have to increase the bounds.  OSL had a heuristic to adjust bounds, maybe we need that here.Clp can use any pivot selection algorithm which the user codes as long as it implements the features in the abstract pivot base class.  The Dantzig method is implemented to show a simple method but its use is deprecated.  Steepest is the method of choice and there are two variants which keep all weights updated but only scan a subset each iteration. Partial switches this on while automatic decides at each iteration based on information about the factorization.This command solves the continuous relaxation of the current model using the dual steepest edge algorithm.The time and iterations may be affected by settings such as presolve, scaling, crash and also by dual pivot method, fake bound on variables and dual and primal tolerances.Normally the default tolerance is fine, but you may want to increase it a bit if a dual run seems to be having a hard time.  One method which can be faster is to use a large tolerance e.g. 1.0e-4 and dual and then clean up problem using primal and the correct tolerance (remembering to switch off presolve for this final short clean up phase).This stops execution ; end, exit, quit and stop are synonymsThe default is not to use any model which had errors when reading the mps file.  Setting this to 'on' will allow all errors from which the code can recover simply by ignoring the error.  There are some errors from which the code can not recover e.g. no ENDATA.  This has to be set before import i.e. -errorsAllowed on -import xxxxxx.mps.Strategy for extra strong branching 

	0 - normal

	when to do all fractional

	1 - root node

	2 - depth less than modifier

	4 - if objective == best possible

	6 - as 2+4

	when to do all including satisfied

	10 - root node etc.

	If >=100 then do when depth <= strategy/100 (otherwise 5)This will write an MPS format file to the given file name.  It will use the default directory given by 'directory'.  A name of '$' will use the previous value for the name.  This is initialized to 'default.mps'.  It can be useful to get rid of the original names and go over to using Rnnnnnnn and Cnnnnnnn.  This can be done by setting 'keepnames' off before importing mps file.This switches on yet more special options!! The bottom digit is a strategy when to used shadow price stuff e.g. 3 means use until a solution is found.  The next two digits say what sort of dual information to use.  After that it goes back to powers of 2 so -

	1000 - switches on experimental hotstart

	2,4,6000 - switches on experimental methods of stopping cuts

	8000 - increase minimum drop gradually

	16000 - switches on alternate gomory criterionThis switches on creation of extra integer variables to gather all variables with same cost.All bounds <= this value - DEBUGThis switches on feasibility pump heuristic at root. This is due to Fischetti, Lodi and Glover and uses a sequence of Lps to try and get an integer feasible solution. Some fine tuning is available by passFeasibilityPump and also pumpTune. See Rounding for meaning of on,both,beforeThis switches on flow cover cuts (either at root or in entire tree) See branchAndCut for information on options. Can also enter testing values by plusnn (==ifmove)The original cuts - beware of imitations!  Having gone out of favor, they are now more fashionable as LP solvers are more robust and they interact well with other cuts.  They will almost always give cuts (although in this executable they are limited as to number of variables in cut).  However the cuts may be dense so it is worth experimenting (Long allows any length). See branchAndCut for information on options.Will write a glpk solution file to the given file name.  It will use the default directory given by 'directory'.  A name of '$' will use the previous value for the name.  This is initialized to 'stdout' (this defaults to ordinary solution if stdout). If problem created from gmpl model - will do any reports.This can be used to switch on or off all heuristics.  Then you can do individual ones off or on.  CbcTreeLocal is not included as it dramatically alters search.1 says stop heuristic immediately allowable gap reached. Others are for feasibility pump - 2 says do exact number of passes given, 4 only applies if initial cutoff given and says relax after 50 passes, while 8 will adapt cutoff rhs after first solution if it looks as if code is stalling.This is a type of 'crash' which works well on some homogeneous problems. It works best on problems with unit elements and rhs but will do something to any model.  It should only be used before primal.  It can be set to -1 when the code decides for itself whether to use it, 0 to switch off or n > 0 to do n passes.This will read an MPS format file from the given file name.  It will use the default directory given by 'directory'.  A name of '$' will use the previous value for the name.  This is initialized to '', i.e. it must be set.  If you have libgz then it can read compressed files 'xxxxxxxx.gz' or 'xxxxxxxx.bz2'.  If 'keepnames' is off, then names are dropped -> Rnnnnnnn and Cnnnnnnn.Whenever a solution is found the bound on solutions is set to solution (in a minimizationsense) plus this.  If it is not set then the code will try and work one out e.g. if all objective coefficients are multiples of 0.01 and only integer variables have entries in objective then this can be set to 0.01.  Be careful if you set this negative!A primitive way of deciding which node to explore next.  Satisfying each integer infeasibility is expected to cost this much.Whether to keep names from importThis is a gross simplification of 'A Relax-and-Cut Framework for Gomory's Mixed-Integer Cuts' by Matteo Fischetti & Domenico Salvagnin.  This simplification just uses original constraints while modifying objective using other cuts. So you don't use messy constraints generated by Gomory etc. A variant is to allow non messy cuts e.g. clique cuts. So 'only' does this while clean also allows integral valued cuts.  'End' is recommended which waits until other cuts have finished and then does a few passes. The length options for gomory cuts are used.This is a lagrangean relaxation for TwoMir cuts.  See lagomoryCuts for description of options.This switches on a local search algorithm when a solution is found.  This is from Fischetti and Lodi and is not really a heuristic although it can be used as one. When used from Coin solve it has limited functionality.  It is not switched on when heuristics are switched on.Level of detail in Coin branch and Cut outputIf 0 then there should be no output in normal circumstances.  1 is probably the best value for most uses, while 2 and 3 give more information.Set optimization direction to maximizeThe default is minimize - use 'maximize' for maximization.
You can also use the parameters 'direction maximize'.Maximum number of iterations between refactorizationsIf this is at its initial value of 200 then in this executable clp will guess at a value to use.  Otherwise the user can set a value.  The code may decide to re-factorize earlier for accuracy.Maximum number of iterations before stoppingThis can be used for testing purposes.  The corresponding library call
      	setMaximumIterations(value)
 can be useful.  If the code stops on seconds or by an interrupt this will be treated as stopping on maximum iterations.  This is ignored in branchAndCut - use maxN!odes.This is a repeatable way to limit search.  Normally using time is easier but then the results may not be repeatable.Maximum number of solutions to saveMaximum number of solutions to getYou may want to stop after (say) two solutions or an hour.  This is checked every node in tree, so it is possible to get more solutions from heuristics.Set optimization direction to minimizeThe default is minimize - use 'maximize' for maximization.
This should only be necessary if you have previously set maximization You can also use the parameters 'direction minimize'.More more dubious options for mipWhether to use Mixed Integer Rounding cutsThis switches on mixed integer rounding cuts (either at root or in entire tree) See branchAndCut for information on options.Controls if Clpnnnn is printedThe default behavior is to put out messages such as:
   Clp0005 2261  Objective 109.024 Primal infeas 944413 (758)
but this program turns this off to make it look more friendly.  It can be useful to turn them back on if you want to be able to 'grep' for particular messages or if you intend to override the behavior of a particular message.  This only affects Clp not Cbc.reads an initial feasible solution from fileThe MIPStart allows one to enter an initial integer feasible solution to CBC. Values of the main decision variables which are active (have non-zero values) in this solution are specified in a text  file. The text file format used is the same of the solutions saved by CBC, but not all fields are required to be filled. First line may contain the solution status and will be ignored, remaining lines contain column indexes, names and values as in this example:

Stopped on iterations - objective value 57597.00000000
      0  x(1,1,2,2)               1 
      1  x(3,1,3,2)               1 
      5  v(5,1)                   2 
      33 x(8,1,5,2)               1 
      ...

Column indexes are also ignored since pre-processing can change them. There is no need to include values for continuous or integer auxiliary variables, since they can be computed based on main decision variables. Starting CBC with an integer feasible solution can dramatically improve its performance: several MIP heuristics (e.g. RINS) rely on having at least one feasible solution available and can start immediately if the user provides one. Feasibility Pump (FP) is a heuristic which tries to overcome the problem of taking too long to find feasible solution (or not finding at all), but it not always succeeds. If you provide one starting solution you will probably save some time by disabling FP. 

Knowledge specific to your problem can be considered to write an external module to quickly produce an initial feasible solution - some alternatives are the implementation of simple greedy heuristics or the solution (by CBC for example) of a simpler model created just to find a feasible solution. 

Question and suggestions regarding MIPStart can be directed to
haroldo.santos@gmail.com.Yet more dubious options for Simplex - see ClpSimplex.hppYet more dubious ideas for feasibility pumpYet more ideas for Feasibility Pump 
	/100000 == 1 use box constraints and original obj in cleanup
	/1000 == 1 Pump will run twice if no solution found
	/1000 == 2 Pump will only run after root cuts if no solution found
	/1000 >10 as above but even if solution found
	/100 == 1,3.. exact 1.0 for objective values
	/100 == 2,3.. allow more iterations per pass
	 n fix if value of variable same for last n iterations.Do multiple root passes to collect cuts and solutionsDo (in parallel if threads enabled) the root phase this number of times  and collect all solutions and cuts generated.  The actual format is aabbcc where aa is number of extra passes, if bb is non zero then it is number of threads to use (otherwise uses threads setting) and cc is number of times to do root phase.  Yet another one from the Italian idea factory (This time - Andrea Lodi , Matteo Fischetti , Michele Monaci , Domenico Salvagnin , and Andrea Tramontani). The solvers do not interact with each other.  However if extra passes are specified then cuts are collected and used in later passes - so there is interaction there.Whether to try some stupid heuristicReally silly stuff e.g. fix all integers with costs to zero!. Doh option does heuristic before preprocessingThis exercises the unit test for clp and then solves the netlib test set using dual or primal.The user can set options before e.g. clp -presolve off -netlibSolve entire netlib test set with barrierThis exercises the unit test for clp and then solves the netlib test set using barrier.The user can set options before e.g. clp -kkt on -netlibSolve entire netlib test set (dual)This exercises the unit test for clp and then solves the netlib test set using dual.The user can set options before e.g. clp -presolve off -netlibSolve entire netlib test set (primal)This exercises the unit test for clp and then solves the netlib test set using primal.The user can set options before e.g. clp -presolve off -netlibpSolve entire netlib test set with 'best' algorithmThis exercises the unit test for clp and then solves the netlib test set using whatever works best.  I know this is cheating but it also stresses the code better by doing a mixture of stuff.  The best algorithm was chosen on a Linux ThinkPad using native cholesky with University of Florida ordering.Clp will go faster if the matrix can be converted to a network.  The matrix operations may be a bit faster with more efficient storage, but the main advantage comes from using a network factorization.  It will probably not be as fast as a specialized network code.Prints next best saved solution to fileTo write best solution, just use solution.  This prints next best (if exists)  and then deletes it.  This will write a primitive solution file to the given file name.  It will use the default directory given by 'directory'.  A name of '$' will use the previous value for the name.  This is initialized to 'stdout'.  The amount of output can be varied using printi!ngOptions or printMask.What strategy to use to select nodesNormally before a solution the code will choose node with fewest infeasibilities. You can choose depth as the criterion.  You can also say if up or down branch must be done first (the up down choice will carry on after solution). Default has now been changed to hybrid which is breadth first on small depth nodes then fewest.This says how many iterations to spend at root node analyzing problem. This is a first try and will hopefully become more sophisticated.Scale factor to apply to objectiveIf the objective function has some very large values, you may wish to scale them internally by this amount.  It can also be set by autoscale.  It is applied after scaling.  You are unlikely to need this.takes duplicate rows etc out of integer modelNormally export will be done using normal representation for numbers and two values per line.  You may want to do just one per line (for grep or suchlike) and you may wish to save with absolute accuracy using a coded version of the IEEE value. A value of 2 is normal. otherwise odd values gives one value per line, even two.  Values 1,2 give normal format, 3,4 gives greater precision, while 5,6 give IEEE values.  When used for exporting a basis 1 does not save values, 2 saves values, 3 with greater accuracy and 4 in IEEE.Import data from file and do parametricsThis will read a file with parametric data from the given file name and then do parametrics.  It will use the default directory given by 'directory'.  A name of '$' will use the previous value for the name.  This is initialized to '', i.e. it must be set.  This can not read from compressed files. File is in modified csv format - a line ROWS will be followed by rows data while a line COLUMNS will be followed by column data.  The last line should be ENDATA. The ROWS line must exist and is in the format ROWS, inital theta, final theta, interval theta, n where n is 0 to get CLPI0062 message at interval or at each change of theta and 1 to get CLPI0063 message at each iteration.  If interval theta is 0.0 or >= final theta then no interval reporting.  n may be missed out when it is taken as 0.  If there is Row data then there is a headings line with allowed headings - name, number, lower(rhs change), upper(rhs change), rhs(change).  Either the lower and upper fields should be given or the rhs field. The optional COLUMNS line is followed by a headings line with allowed headings - name, number, objective(change), lower(change), upper(change).  Exactly one of name and number must be given for either section and missing ones have value 0.0.Number of cut passes at root nodeThe default is 100 passes if less than 500 columns, 100 passes (but stop if drop small if less than 5000 columns, 20 otherwiseHow many passes in feasibility pumpThis fine tunes Feasibility Pump by doing more or fewer passes.Normally Presolve does 10 passes but you may want to do less to make it more lightweight or do more if improvements are still being made.  As Presolve will return if nothing is being taken out, you should not normally need to use this fine tuning.Perturbation helps to stop cycling, but Clp uses other measures for this.  However large problems and especially ones with unit elements and unit rhs or costs benefit from perturbation.  Normally Clp tries to be intelligent, but you can switch this off.  The Clp library has this off by default.  This program has it on by default.Whether to use Product Form of Inverse in simplexBy default clp uses Forrest-Tomlin L-U update.  If you are masochistic you can switch it off.Whether to try Pivot and Complement heuristicstuff needed. Doh option does heuristic before preprocessingWhether to try Pivot and Fix heuristicClp will go slightly faster if the matrix can be converted so that the elements are not stored and are known to be unit.  The main advantage is memory use.  Clp may automatically see if it can convert the problem so you should not need to use this.If this is > 0 then presolve will give more information and branch and cut will give statisticsPresolve analyzes the model to find such things as redundant equations, equations which fix some variables, equations which can be transformed into bounds etc etc.  For the initial solve of any problem this is worth doing unless you know that it will have no effect.  on will normally do 5 passes while using 'more' will do 10.  If the problem is very large you may need to write the original to file using 'file'.Whether to use integer preprocessingThis tries to reduce size of model in a similar way to presolve and it also tries to strengthen the model - this can be very useful and is worth trying.  Save option saves on file presolved.mps.  equal will turn <= cliques into ==.  sos will create sos sets if all 0-1 in sets (well one extra is allowed) and no overlaps.  trysos is same but allows any number extra.  equalall will turn all valid inequalities into equalities with integer slacks.  strategy is as on but uses CbcStrategy.The default is 1.0e-8 - you may wish to try 1.0e-7 if presolve says the problem is infeasible and you have awkward numbers and you are sure the problem is really feasible.Clp can use any pivot selection algorithm which the user codes as long as it implements the features in the abstract pivot base class.  The Dantzig method is implemented to show a simple method but its use is deprecated.  Exact devex is the method of choice and there are two variants which keep all weights updated but only scan a subset each iteration. Partial switches this on while change initially does dantzig until the factorization becomes denser.  This is still a work in progress.This command solves the continuous relaxation of the current model using the primal algorithm.  The default is to use exact devex. The time and iterations may be affected by settings such as presolve, scaling, crash and also by column selection  method, infeasibility weight and dual and primal tolerances.For an optimal solution no primal infeasibility may exceed this valueNormally the default tolerance is fine, but you may want to increase it a bit if a primal run seems to be having a hard timeInitially algorithm acts as if it costs this much to be infeasibleThe primal algorithm in Clp is a single phase algorithm as opposed to a two phase algorithm where you first get feasible then optimal.  So Clp is minimizing this weight times the sum of primal infeasibilities plus the true objective function (in minimization sense).  Too high a value may mean more iterations, while too low a bound means the code may go all the way and then have to increase the weight in order to get feasible.  OSL had a heuristic to adjust bounds, maybe we need that here.This changes the amount and format of printing a solution:
normal - nonzero column variables 
integer - nonzero integer column variables
special - in format suitable for OsiRowCutDebugger
rows - nonzero column variables and row activities
all - all column variables and row activities.

For non-integer problems 'integer' and 'special' act like 'normal'.  Also see printMask for controlling output.Control printing of solution on a  maskIf set then only those names which match mask are printed in a solution. '?' matches any character and '*' matches any set of characters.  The default is '' i.e. unset so all variables are printed. This is only active if model has names.Import priorities etc from fileThis will read a file with priorities from the given file name.  It will use the default directory given by 'directory'.  A name of '$' will use the previous value for the name.  This is initialized to '', i.e. it must be set.  This can not read from compressed files. File is in csv format with allowed headings - name, number, priority, direction, up, down, solution.  Exactly one of name and number must be given.This switches on probing cuts (either at root or in entire tree) See branchAndCut for information on options. but strong options do more probingWhether to do proximity search heuristicThis switches on a heuristic which looks for a solution close to incumbent solution (Fischetti and Monaci). See Rounding for meaning of on,both,before. Can also set different maxNode settings by plusnnnn (and are 'on'(on==30)).Fake cutoff for use in feasibility pump0.0 off - otherwise add a constraint forcing objective below this value in feasibility pumpFake increment for use in feasibility pump0.0 off - otherwise use as absolute increment to cutoff when solution found in feasibility pumpDubious ideas for feasibility pumpThis fine tunes Feasibility Pump 
	>=10000000 use as objective weight switch
	>=1000000 use as accumulate switch
	>=1000 use index+1 as number of large loops
	==100 use objvalue +0.05*fabs(objvalue) as cutoff OR fakeCutoff if set
	%100 == 10,20 affects how each solve is done
	1 == fix ints at bounds, 2 fix all integral ints, 3 and continuous at bounds. If accumulate is on then after a major pass, variables which have not moved are fixed and a small branch and bound is tried.This stops the execution of Clp, end, exit, quit and stop are synonymsThis sets a random seed for Cbc - 0 says use time of day, -1 is as now.Whether to try randomized rounding heuristicThis sets a random seed for Clp - 0 says use time of day.Stop when gap between best possible and best less than this fraction of larger of twoIf the gap between best solution and best possible solution is less than this fraction of the objective value at the root node then the search will terminate.  See 'allowableGap' for a way of using absolute value rather than fraction.This will read a binary solution file from the given file name.  It will use the default directory given by 'directory'.  A name of '$' will use the previous value for the name.  This is initialized to 'solution.file'.  This reads in a file from saveSolutionScale factor to apply to objective in placeYou can set this to -1.0 to test maximization or other to stress codeWhether to use Reduce-and-Split cutsThis switches on reduce and split  cuts (either at root or in entire tree). May be slow See branchAndCut for information on options.Whether to use Reduce-and-Split cuts - style 2This switches on reduce and split  cuts (either at root or in entire tree) This version is by Giacomo Nannicini based on Francois Margot's version Standard setting only uses rows in tableau <=256, long uses all May be slow See branchAndCut for information on options.This switches on reduce and split  cuts (either at root or in entire tree) This version is by Giacomo Nannicini based on Francois Margot's version Standard setting only uses rows in tableau <=256, long uses all See branchAndCut for information on options.Whether to use Residual Capacity cutsResidual capacity cuts. See branchAndCut for information on options.Restore model from binary fileThis reads data save by saveModel from the given file.  It will use the default directory given by 'directory'.  A name of '$' will use the previous value for the name.  This is initialized to 'default.prob'.Useful for testing if maximization works correctlyScale factor to apply to rhs and boundsIf the rhs or bounds have some very large meaningful values, you may wish to scale them internally by this amount.  It can also be set by autoscale.  This should not be needed.Whether to try Relaxation Enforced Neighborhood SearchThis switches on Relaxation enforced neighborhood Search. on just does 50 nodes 200 or 1000 does that many nodes. Doh option does heuristic before preprocessingWhether to try Relaxed Induced Neighborhood SearchThis switches on Relaxed induced neighborhood Search. Doh option does heuristic before preprocessingWhether to use Rounding heuristicThis switches on a simple (but effective) rounding heuristic at each node of tree.  On means do in solve i.e. after preprocessing, Before means do if doHeuristics used, off otherwise, and both means do if doHeuristics and in solve.This will save the problem to the given file name for future use by restoreModel.  It will use the default directory given by 'directory'.  A name of '$' will use the previous value for the name.  This is initialized to 'default.prob'.This will write a binary solution file to the given file name.  It will use the default directory given by 'directory'.  A name of '$' will use the previous value for the name.  This is initialized to 'solution.file'.  To read the file use fread(int) twice to pick up number of rows and columns, then fread(double) to pick up objective value, then pick up row activities, row duals, column activities and reduced costs - see bottom of CbcOrClpParam.cpp for code that reads or writes file. If name contains '_fix_read_' then does not write but reads and will fix all variablesScaling can help in solving problems which might otherwise fail because of lack of accuracy.  It can also reduce the number of iterations.  It is not applied if the range of elements is small.  When unscaled it is possible that there may be small primal and/or infeasibilities.After this many seconds coin solver will act as if maximum nodes had been reached.If passed to solver fom ampl, then ampl will wait so that you can copy .nl file for debug.Maximum number of tries for slower cutsSome cut generators are fairly slow - this limits the number of times they are tried.Number of slp passes before primalIf you are solving a quadratic problem using primal then it may be helpful to do some sequential Lps to get a good approximate solution.Whether to use small factorizationIf processed problem <= this use small factorizationThis will write a primitive solution file to the given file name.  It will use the default directory given by 'directory'.  A name of '$' will use the previous value for the name.  This is initialized to 'stdout'.  The amount of output can be varied using printi!ngOptions or printMask.If there are no integer variables then this just solves LP.  If there are integer variables this does branch and cut.Normally if AMPL says there are SOS variables they should be used, but sometime sthey should be turned off - this does so.Level of detail in (LP) Solver outputIf 0 then there should be no output in normal circumstances.  1 is probably the best value for most uses, while 2 and 3 give more information.  This parameter is only used inside MIP - for Clp use 'log'Whether factorization treated as sparseDubious options for Simplex - see ClpSimplex.hppFor long and thin problems this program may solve a series of small problems created by taking a subset of the columns.  I introduced the idea as 'Sprint' after an LP code of that name of the 60's which tried the same tactic (not totally successfully).  Cplex calls it 'sifting'.  -1 is automatic choice, 0 is off, n is number of passesThis command prints some statistics for the current model. If log level >1 then more is printed. These are for presolved model if presolve on (and unscaled).Switches on groups of featuresThis turns on newer features. Use 0 for easy problems, 1 is default, 2 is aggressive. 1 uses Gomory cuts using tolerance of 0.01 at root, does a possible restart after 100 nodes if can fix many and activates a diving and RINS heuristic and makes feasibility pump more aggressive. This does not apply to unit tests (where 'experiment' may have similar effects).Number of variables to look at in strong branchingIn order to decide which variable to branch on, the code will choose up to this number of unsatisfied variables to do mini up and down branches on.  Then the most effective one is chosen. If a variable is branched on many times then the previous average up and down costs may be used - see number before trust.How long a column to substitute for in presolveNormally Presolve gets rid of 'free' variables when there are no more than 3  variables in column.  If you increase this the number of rows may decrease but number of  elements may increase.Tighten bounds using this times largest activity at continuous solutionThis sleazy trick can help on some problems.Poor person's preSolve for nowWhether to use CPU or elapsed timecpu uses CPU time for stopping, while elapsed uses elapsed time. (On Windows, elapsed time is always used).Number of branches before we trust pseudocostsUsing strong branching computes pseudo-costs.  After this many times for a variable we just trust the pseudo costs and do not do any more strong branching.Format aabbcccc - 
 If aa then this is number of major passes (i.e. with presolve) 
 If bb and bb>0 then this is number of minor passes (if unset or 0 then 10) 
 cccc is bit set 
 0 - 1 Heavy probing 
 1 - 2 Make variables integer if possible (if obj value)
 2 - 4 As above but even if zero objective value
 7 - 128 Try and create cliques
 8 - 256 If all +1 try hard for dominated rows
 10 - 1024 Use a larger feasibility tolerance in presolve
 11 - 2048 Try probing before creating cliques
 12 - 4096 Switch off duplicate column checking for integersWhether to use Two phase Mixed Integer Rounding cutsThis switches on two phase mixed integer rounding  cuts (either at root or in entire tree) See branchAndCut for information on options.Vnd!VariableNeighborhoodSearchIf set will try and fix some integer variablesThis switches on zero-half cuts (either at root or in entire tree) See branchAndCut for information on options.  This implementation was written by Alberto Caprara.Kill all coefficients whose absolute value is less than this valueý4öÿ5öÿ¬5öÿý4öÿÄ5öÿÜ5öÿô5öÿ6öÿ6öÿ7öÿ7öÿ 7öÿà6öÿð6öÿ\8öÿ<8öÿ,8öÿ8öÿq7öÿq7öÿq7öÿq7öÿq7öÿq7öÿq7öÿ8öÿq7öÿq7öÿq7öÿq7öÿq7öÿq7öÿì7öÿÜ7öÿì9öÿ:öÿ:öÿÜ9öÿÜ9öÿÜ9öÿÜ9öÿÜ9öÿÜ9öÿÜ9öÿ:öÿÜ9öÿÜ9öÿÜ9öÿÜ9öÿÜ9öÿÜ9öÿ,:öÿ<:öÿ=öÿ°=öÿ°=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ=öÿ>öÿð=öÿä=öÿ_=öÿg=öÿÒ=öÿÀ=öÿ8>öÿH>öÿH>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿ8>öÿX>öÿh>öÿx>öÿ>öÿ>öÿ¨>öÿ¸>öÿq?öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿV?öÿ>öÿJ?öÿ/?öÿ?öÿ>öÿ>öÿ>öÿ>öÿ?öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ?öÿ>öÿó>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿ>öÿu>öÿÛ>öÿÌ>öÿD?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿT?öÿ4?öÿd?öÿl?öÿt?öÿ4?öÿ4?öÿ4?öÿ4?öÿ?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ?öÿ4?öÿ?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ4?öÿ¤?öÿ´?öÿÄ?öÿ¬]öÿ¤]öÿ]öÿ]öÿ]öÿ|]öÿr]öÿh]öÿÔ^öÿÌ^öÿÂ^öÿ¸^öÿ®^öÿ¤^öÿ^öÿ^öÿü_öÿô_öÿê_öÿà_öÿÖ_öÿÌ_öÿÂ_öÿ¸_öÿ$aöÿaöÿaöÿaöÿþ`öÿô`öÿê`öÿà`öÿ.AÀÿÿÿßAà?�÷ư>ð¿ÿÿÿÿ@µx¯D}Ô%I²TDóÂääécLDóÂääéc̚d~ÅQJ}Ô%I²Ô¢mB#B¡Ç;4&õkCð?@µx¯Ěñ?ñh㈵øä>ü©ñÒMbP?0ä.ÿ++ÿÿÿÿÿÿÿèÿÿÿÿÿÿÿÿIf the gap between best solution and best possible solution is less than this then the search will be terminated.  Also see ratiSet basis back to all slack and d reset solutionCosts >= this treated as artificials in feasibil0.0 off - otherwise variables with costs >= this are treated as artificials and fixed to lower bound in feasibilWhether to scale objective, rhs and bounds of problem if they loSolve using primal dual predictor corrector algoThis command solves the current model using the  primal dual predictor corrector algorithm.  You may want to link in an alternative ordering and factorization.  It will also solve models with quadratic objectExport basis as This will write an MPS format basis file to the given file name.  It will use the default directory given by 'directory'.  A name of '$' will use the previous value for the name.  This is initialized to 'defao 'default.bas'.Whether factorization biased towDo Branch and Cu in barrier (and ordering speed)Which cholesky aUni!versityOfFloyOfFlorida_dummyWhether to use C use Clique cutsThis switches on clique cuts (either at root or in entire tree) See branchAndCut for informationtion on options.Whether to use combine solution lution heuristiccombine!Solutionrossover solutiocombine2!Solutio a heuristic which does branch and cut on the problem given by fixing variables which have same value in two or more solutions. It obviously only tries after two or more solutions. See Rounding for meaning off on,both,beforeutoff as constraconstraint!fromCThis adds the objective as a constraint with best solution as RHHow to use coststs as prioritiesThis orders the variables in order of their absolute costs - with largest cost ones being branched on first.  This primitive strategy can be surprsingly effective.  The column order option is obviously not on costs but easy sy to code here.Generates C++ coWhether to create basis for probWhether to get a basic solution on after barrierCreate one line ne of statisticsThis appends statistics to given file name.  It will use the default directory given by 'directory'.  A name of '$' will use the previous value for the name.  This is initialized to '', i.e. it must be set.  Adds header if file empty or doe does not exist.All solutions must be better thaSwitches all cutl cuts on or offread valid solutlution from file0 - off, 1 choose blocks >1 use as blocks Dantzig Wolfe if primal, Benders if dual - uses sprint pass for numbernumber of passesWhether to use dense factorizatiIf processed problem <= this use dense factorizaDepth at which t to try mini BABExtra double par Distance induced neighborhood Search. See Rounding for meaning of on,both,beforSet Default directory for importThis sets the directory which import, export, saveModel, restoreModel etc will use.  It is inititialized to './'rectory where the COIN-OR sample problems reside. It is used only when -unitTest is passed to clp. clp will pick up the test problems from this directory. It is initialized to '../../Data/SampSet directory where the netlib pib problems are.ere the miplib 2003 problems are a random diving heuristic at various times. C - Coefficient, F - Fractional, G - Guided, L - LineSearch, P - PseudoCost, V - VectorLength. You may prefer to use individual on/off See Rounding for meaning of Whether to try D DiveCoefficienty DiveFractionalDivingF!ractionao try DiveGuidedy DiveLineSearchDivingL!ineSearcy DivePseudoCostDiveVectorLengthDivingV!ectorLenDo heuristics before any preprocInitially algorithm acts as if no gap between bounds exceeds thiDon't even think think about it.Do dual simplex implex algorithmFor an optimal solution no dual infeasibility may exceed this vatry DW heuristicSee Rounding for meaning of on,bDo dual or primal simplex algorives the continuous relaxation of the current model using the dual or primal algorithm, based on a dubious analysalysis of model.Stops clp executRead commands frfrom environmentThis starts reading from environment variable CBC_CLP_ENVIRONMENWhether to allowow import errorsThis stops the execution of Clp, end, exit, quit and stop are syWhether to use ttesting featuresDefines how adventurous you want to be in using new ideas. 0 then no new ideas, 1 fairly sensible, 2 a bit dubious, 3 you are onare on your own!Whether to do even more strong bExport model as Extra integer paeger parameter 1eger parameter 2eger parameter 3eger parameter 4Allow creation of extra integer nteger variablesWhich factorizatorization to useThe default is to use the normal CoinFactorization, but other choices are a dense one, osl's or one designed for small problems.Whether to try FFeasibility Pumpfeas!ibilityPumpTry heuristic based on fixing variables with reduced costs greatreater than thisIf this is set i with reduced costs greater than this will be fixed before branch and bound - use with extreme cWhether to use F Flow Cover cutsWhether to use given solution asas crash for BAB-1 off.  If 1 then tries to branch to solution given by AMPL or priorities file. If 0 then just tries to set as best solution If >1 then also does that many nodes on fixed probAfter a pass in feasibility pump, variables which have not moved about are fixed and if the preprocessed model is small enough a few nodes of branch and bound are done on reduced problem.  Small problem has to be less than this fraction of ion of original.Whether to use alternative Gomor an alternative Gomory cut generator (either at root or in entire tree) This version is by Giacomo Nannicini and may be more robust See branchAndCut for informaWhether to use G use Gomory cuts greedy heuristiSwitches on a greedy heuristic which will try and obtain a solution.  It may just fix a percentage of variables and then try a small branch and cut run. See Rounding for meaning of on,both,befPuts glpk solutisolution to fileSwitches most heuristics on or oheur!isticsOnOffPrint out version, non-standard options and someThis prints out some help to get user started.  If you have printed this then you should be pastst that stage:-)Maximum iteratioons on hot startWhether to try i try idiot crashA valid solution must be at least this much better than last intinteger solutionEach integer infeasibility is expected to cost tinf!easibilityWeSolve to continuThis just solves the problem to continuous - without adding any olution no integer variable may be this away from an integer valintegerT!olerancBeware of setting this smaller than the primal trimal tolerance.It saves space to get rid of names so if you need to you can set this to off.  This needs to be set before the import of model - so -keepnames off -import xxxxxWhether to use KKT factorizationse Knapsack cuts knapsack cuts (either at root or in entire tree) See branchAndCut for informatiWhether to use Lagrangean Gomoryagrangean TwoMirift and Project lift!AndProjectCLift and project cuts. May be slow See branchAndCut for informatWhether to use llocal treesearchThis exercises the unit test forThere are times e.g. when using AMPL interface when you may wish to do something unusual.  Look for USERCLP in main driver and modify sample codHand coded Cbc sfor USERCBC in me. It is possible you can get same effect by using example driveWhether to try Variable Neighborghborhood Search variable neighborhood Search. Doh option does heuristic before re preprocessingWhether to use vector? Form of matrix in simplexIf this is on ClpPackedMatrix uses extra column copy in odd formSwitches on longer help on singlSet to 1 to get short help with ? list, 2 to get long help, 3 for both.  (add 4 to just get amplType of vub heurWhether to use ze zero half cutsThis applies to reading mps files (and also lp files if KILL_ZERO_READLP defined** SOS number of members reduced from %d to %d!
OsiBiLinear would branch %s on %c variable %d from value %g
 - at %g, free range %d (%g) => %d (%g), %d would be fixed, %d other way
one of x and y must have a mesh sizenot adjacent - presuming small djsWeights too close together in OsiLinkThere are %d bilinear and %d integers
lower bound on %d changed from %g to %g
upper bound on %d changed from %g to %g
There were %d I-I, %d I-x and %d x-x bilinear in objective
There were %d I-I, %d I-x and %d x-x bilinear in total of which %d were duplicates
Model not set up or nonlinear arrays not created!Pass %d, objective %g - drop %g maxDelta %g
Exiting as maxDelta < tolerance and small dropProblem is infeasible - tightenPrimalBounds!Pre-processing says infeasible!downupSOS DownSOS Uplost setbad nonlinear term %s
 for x %d y %d - pi %g %g
(%d %g) Numericbad x mesh %g %g %g -> %g
bad y mesh %g %g %g -> %g
can add row %d
better qp objective of %g
obj of %g
QP says expensive - killinfeasible row - reformulateUnable to modify matrixUnable to use prioritiesOsiLinkinfeasibilityThere are %d integers
all bilinearity in objective***** offset %g
last good %d goodMove %d
Exiting as all gaps smallExiting on target drop %g
%d bounds etc %g %g %g
pass%d.mpserror or linear - fix %d
ProbingGomoryKnapsackCoverCliqueMixedIntegerRounding2FlowCoverTwomirobj %g
processed model has  rows,  and cutoff before fathomingpossibleBB best value %g
*** WHY BAD SOL in :: method  : assertion '' failed.Possible reason: ëYøÿäYøÿÜYøÿÔYøÿÌYøÿÄYøÿ¼Yøÿ´YøÿiøÿühøÿôhøÿìhøÿähøÿÜhøÿÔhøÿÌhøÿÕiøÿÎiøÿÆiøÿ¾iøÿ¶iøÿ®iøÿ¦iøÿiøÿsùøÿlùøÿdùøÿ\ùøÿTùøÿLùøÿDùøÿ<ùøÿóøÿóøÿróøÿeóøÿXóøÿKóøÿ>óøÿ1óøÿ×ùÿÔùÿÐùÿÌùÿÈùÿÄùÿÀùÿ¼ùÿ"ùÿû!ùÿô!ùÿí!ùÿæ!ùÿß!ùÿØ!ùÿÑ!ùÿ"ùÿØ'ùÿ`'ùÿY'ùÿ"ùÿã'ùÿ"ùÿ"ùÿw3ùÿ®:ùÿ¢:ùÿ:ùÿl3ùÿd3ùÿ\3ùÿT3ùÿsEùÿqEùÿkEùÿeEùÿ_EùÿYEùÿSEùÿMEùÿ}Dùÿ{DùÿuDùÿoDùÿiDùÿcDùÿ]DùÿWDùÿqEùÿkEùÿdEùÿ_EùÿZEùÿSEùÿLEùÿEEùÿ¯Kùÿ¬Kùÿ¨Kùÿ¤Kùÿ KùÿKùÿKùÿKùÿLùÿLùÿ|LùÿuLùÿnLùÿgLùÿ`LùÿYLùÿùÿþùÿÙ{ùÿÑ{ùÿÉ{ùÿÁ{ùÿ¹{ùÿ±{ùÿF~ùÿ?~ùÿ7~ùÿ/~ùÿ'~ùÿ~ùÿ~ùÿ~ùÿùÿùÿvùÿnùÿWùÿOùÿ
ùÿùÿۑùÿԑùÿ̑ùÿđùÿ¼ùÿ´ùÿ¬ùÿ¤ùÿBùÿ²ùÿ'ùÿ¦ùÿùÿ¾ùÿ¾ùÿ13OsiSolverLink20CbcHeuristicDynamic310OsiOldLink25OsiOldLinkBranchingObject11OsiBiLinear26OsiBiLinearBranchingObject19OsiBiLinearEquality21OsiSimpleFixedInteger15OsiUsesBiLinear21OsiChooseStrongSubset12CglTemporary28OsiSolverLinearizedQuadratic}Ô%I²T-Cëâ6?�÷ư>�÷ư>ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ	ÿÿÿÿÿÿÿÿð?ÿÿÿÿÿÿÿÿ    	

ÿÿÿÿÿÿïê-q=0CH¯¼ò×z>ÿÿÿÿÿÿïÿê-q½jø@è'R¸ëQà?@¬ҶOɃý;Ð?$@+¡=ø?ËH¿}M?$°ï_F _ B@@ꌠ9Y>)Æꌠ9Y>)FeÍÍAV瞯Ò<V瞯Ҽ+ÙÎ÷ï?ñh㈵ø侚É?wJëÿï?חAvIhÂ%<=¹?q=
ףì°@ÿÿÿ©?@ ؗA{®Gáz?°¦A.MPS.mps.gz.MPS.gz.MPS.GZ.mps.bz2.MPS.bz2.MPS.BZ2p0033  processing mps file: ERROR: Cannot find MPS file  unscaled  was tried  times and created  cuts of which  times out of  solutions.cbc_clp (; okay != ; error = ) status not optimal; assuming too many nodes -- ( n /  i /  s) (subtotal cbc_clp solved  stopped on nodes) gave bad answer!) and took  seconds.****** Unit Test failed.****** Unit Test succeeded.Not doing miplib run as can't find mps files.Perhaps you're trying to read gzipped (.gz) files without libz?Solving miplib problems in sets Largest (scaled) away from bound Row cut debugger activated for Failed to activate row cut debugger for Cuts at root node changed objective from  were active after adding rounds of cuts solutions found by heuristics.Fast node size Columns %d rows %d - depth %d
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%s has %d entries
%d between %g and %g%d exactly at %guser_driver.cpp
  // %s

clpMo_OsiC++ file written to %s
./../../Data/Sample/../../Data/Netlib/../../Data/miplib3/.\..\..\..\..\Data\Sample\..\..\..\..\Data\Netlib\..\..\..\..\Data\miplib3\==**UsefulNoGood==== for integers %d zero objective absolute objective values %d different
_int_abs===== end objective countsThere are %d singletons with objective ** %d columns have no entriesColumn breakdown:%d of type %s Row breakdown:%d columns have %d entries

    .........
column %d %g <= ( %d, %g) Singleton free %d - cost %g
%d master rows %d <= < %d
fake.bndblock%d.mps FX BOUND1    %.8s  %g
***** Problem is infeasible** %d rows have no entries
XX %d rows have %d entries
row %d %g <= possible %d - %d integers
bad colbad row%d columns, %d rows
%d,%g ElementsRowLowerRowUpperColumnLowerColumnUpperObjective-logVersion: %s 
Sep  9 2024Build Date: %s 
stratbranchNo match for abcd value sets valueCommands are:Command ---- description----Full list of commands is:%d columns fixed
 value remains  has value using OSL factorization -passc 1000!No nonlinear solution
Result - %s%s

RedSplitcombine solutionsgreedy covergreedy equalitydynamic pass thruunboundedstoppedotherProblem is %s - %.2f seconds************No objective!!presolved.mps%d Integer slacks addedGomoryL1GomoryL2KnapsackReduce-and-splitReduce-and-split(2)Gomory(2)TwoMirCutsTwoMirCutsL1TwoMirCutsL2LiftAndProjectResidualCapacityZeroHalfBAD debug fileBadmore mip options %dXXXXX searchStrategy %d
XXXXX analyze %d
C%7.7dSet %d is SOS - priority %d
Obj %d is int? - priority %d
Testing OsiObject options %dlinkednlp options %d
StoredOnceOnlybilinear priority now %d
Heuristic failed%d cuts dropped
Partial solution givencode thisglobal.cutsglobal.fixStarting pass %dTesting OsiObject options %d
createAfterPre (%.3f seconds)create
Result - %s%s
stdin_lpHOMEUnable to open file %sThere were %d errors on inputSaving presolved model on %s
Saving model on %s
.lpmask_Odd file .mst.MST.csvendataENDATA%d fields and %d records %d bad pseudo costs %d bad directions %d bad prioritiesopening mipstart file %s..before.%d %g
Setting %s to DEBUG %genter value for model really really scaledCbc version , build Non default values:-Perturbation  (default 100),runtime_options,%s,%d-csvappend$stdoutstderr%d %d
%d 2 %g
4 %g 1.0
%d %g %g
Optimal (within gap tolerance)InfeasibleUnboundedStopped on timeStopped on iterationsStopped on difficultiesStopped on ctrl-cInteger infeasibleStatus unknown - objective value %.8f
mismatched " in mask %s
mismatched ' in mask %s
mask %s too long - skipping
Bound rangingRhs rangingObjective ranging,nameNo names match - doing all%d,%s,%.10g,C%7.7d,R%7.7d,1.0e100,,R%7.7d1.0e100, %s         %s
** %7d number,solution
%7d, %15.8g	int intIndicesV[]={
};
%d 	double intSolnV[]={
%d.  - ? for list of commandsShort match for  - completion:  : Multiple matches for  - possible completions:Completions of cbcÿÿÿÿÿÿïÿ4&õkÃèvH7ÂחÁjøÀÃÀ@ÀYÀ$À𿚙¹¿{®Gáz¿ü©ñÒMbP¿-Cëâ6¿ñh㈵øä¾:0âyE¾dyáý¥½V瞯ҼV瞯Ò<dyáý¥=:0âyE>ñh㈵øä>-Cëâ6?ü©ñÒMbP?{®Gáz?¹?ð?$@Y@@@Ã@jø@חAèvH7B4&õkCÿÿÿÿÿÿï,$üÿ'$üÿ $üÿ$üÿ$üÿ$üÿ$üÿý#üÿ]Àüÿ]ÀüÿµÂüÿÂüÿYÂüÿ-ÂüÿÉÁüÿZÁüÿ¤þÿYþÿûþÿNþÿþÿêþÿ¸þÿzþÿYþÿ3þÿþÿùþÿàþÿÕþÿÊþÿ¿þÿçþÿÜþÿòþÿðþÿåþÿËþÿ¦Åüÿðþÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ¦Åüÿ?þÿ¦Åüÿþÿ_þÿMþÿ;þÿ)þÿþÿÒÅüÿ¦ÅüÿóþÿþÿþÿsþÿaþÿêýýÿÒÅüÿÒÅüÿÒÅüÿ¦ÅüÿÒÅüÿ´þÿ¢þÿ¦ÅüÿýÝüÿ¦ÅüÿÒÅüÿÒÅüÿÒÅüÿÒÅüÿÒÅüÿÒÅüÿÒÅüÿ¦ÅüÿÒÅüÿÒÅüÿÒÅüÿëÝüÿÒÅüÿ¦ÅüÿÒÅüÿ¦Åüÿ¦Åüÿ¦ÅüÿØýýÿþÿ¢ÅüÿÅüÿÅüÿ~ÅüÿrÅüÿfÅüÿÏÄüÿZÅüÿNÅüÿEÅüÿ<Åüÿ3ÅüÿÈÄüÿ9ÇüÿÆÆüÿÆÆüÿ9ÇüÿÆÆüÿÆÆüÿÆÆüÿÆÆüÿ*ÇüÿÆÆüÿ*ÇüÿÆÆüÿÆÆüÿÆÆüÿÆÆüÿÆÆüÿÆÆüÿ|ÔüÿÆÆüÿÆÆüÿÆÆüÿÆÆüÿÈÌüÿÆÆüÿÆÆüÿ~ÑüÿÆÆüÿÆÆüÿÆÆüÿ*Çüÿmýÿýÿýÿ)ýÿ^ ýÿÙýÿÿýÿÎýÿ¡ýÿ¡ýÿõÿýÿkýÿõÿYýÿ
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ýÿ¡ýÿõÿõÿ6*ýÿZïüÿ¯ûÿDîüÿD8ýÿ¯ûÿãéüÿ&æüÿõÿ½ýÿôüÿÖúüÿõÿõÿõÿõÿõÿõÿõÿõÿõÿõÿõÿV8ýÿïåüÿõÿ§àüÿ¬Üüÿ¯ûÿü
üÿÖúüÿÊ@ýÿÅ@ýÿ¾@ýÿ·@ýÿ°@ýÿ©@ýÿ¢@ýÿ@ýÿ7Cýÿ2Cýÿ+Cýÿ$CýÿCýÿCýÿCýÿCýÿºýÿµýÿ®ýÿ§ýÿ ýÿýÿýÿýÿáýÿ´ýÿZýÿ-ýÿýÿêýÿýÿЈýÿGýÿýÿ:ýÿ
ýÿöýÿʈýÿzýÿ°ýÿÊÁüÿóþÿþÿÊþÿ¤þÿ~þÿIþÿuþÿoþÿhþÿaþÿZþÿSþÿLþÿEþÿa!þÿ[!þÿS!þÿK!þÿC!þÿ;!þÿ3!þÿ+!þÿU$þÿN$þÿE$þÿ<$þÿ3$þÿ*$þÿ!$þÿ$þÿU&þÿN&þÿE&þÿ<&þÿ3&þÿ*&þÿ!&þÿ&þÿ7(þÿ0(þÿ((þÿ (þÿ(þÿ(þÿ(þÿ(þÿõ)þÿî)þÿå)þÿÜ)þÿÓ)þÿÊ)þÿÁ)þÿ¸)þÿ¥+þÿ+þÿ+þÿ+þÿ+þÿz+þÿq+þÿh+þÿ'd'dÈd'dd'dÈ5    process.preProcessNonDefault(*saveSolver,%d,10);Original problem has %d integers (%d of which binary)
Presolved problem has %d integers (%d of which binary)
%d variables have objective of %g


Problem has %d rows, %d columns (%d with objective) and %d elements
%d singletons with no objective XX %d columns have %d entries
Doubleton free %d - cost %g - %g in %srow with %d entries and %g in %srow with %d entries
Singleton %d with no objective in row with %d elements - rhs %g,%g
Singleton %d (%s) with objective in row %d (%s) with %d equal elements - rhs %g,%g
%d singletons with zero cost, %d with valid cost
%s %d blocks (largest %d,%d), %d master rows (%d empty) out of %d, %d master columns (%d empty) out of %d
Block %d has %d rows and %d columns (%d elements)
BBB objective %g for block %d
ouch - did not check out as goodAdding %d rows with %d elements
CoinSolver takes input from arguments ( - switches to stdin)Enter ? for list of commands or helpIn argument list keywords have leading - , -stdin or just - switches to stdinOne command per line (and no -)abcd? gives list of possibilities, if only one + explanationabcd?? adds explanation, if only one fuller helpabcd without value (where expected) gives current valueCbc options are set within AMPL with commands like:         option cbc_options "cuts=root log=2 feas=on slog=1"only maximize, dual, primal, help and quit are recognized without =Multiple for continuous dj fixing is %g
 is illegal for double parameter switching on global root cuts for gomory and knapsackextra options - -rens on -extra4  is illegal for integer parameter Dual of model has %d rows and %d columnsDoing %d presolve passes - picking up non-costed slacksDoing %d presolve passes - picking up costed slacksTime (CPU seconds):         %.2f
Time (Wallclock Seconds):   %.2f
Statistics for presolved modelPresolved model looks infeasible - will use unpresolvedStatistics for unpresolved modelContinuous objective value is %g - %.2f secondsEmptying ampl stored cuts as internal preprocessingcannot create std::vector larger than max_size()Preprocessed model (minimization) on presolved.mpsProblem has %d integers (%d of which binary) and %d continuous%d continuous have nonzero objective, %d have zero objective%d integer have nonzero objective, %d have zero objective, %d different nonzero (taking abs)%d variables have objective of %gReplacing model - %d new variablesObjective integer added with %d elements and %d Integer slacks addedObjective integer added with %d elementsdebug file has incorrect number of columns%d SOS variables vanished due to pre processing? - check validity?Unable to open file user_driver.cpp*** Temp heuristic with mode %d
Starting %d passes each with %d solversEnding major passes - best solution %gCuts at root node changed objective from %g to %g%s was tried %d times and created %d cuts of which %d were active after adding rounds of cuts%d bounds tightened after postprocessing
Accuracy problem on post-processing - maybe try without pre-processingRestoring heuristic best solution of %gObjective value:                %.8f
%s bound:                    %.3f
Gap:                            %.2f
Enumerated nodes:               %d
Total iterations:               %d
Time (CPU seconds):             %.2f
Time (Wallclock seconds):       %.2f
GMPL model file %s and data file %s
Presolved model looks infeasible - saving original on %s
 ** %d records did not match on name/sequenceDuplicate or unknown keyword - or name/number fields wrongfile %s will be used before preprocessing.Will create a debug file so this run should be a good one%d doubles read into debugValues
unit test is now only from clp - does same thingPresolve being done with 5 passes
Dual steepest edge steep/partial on matrix shape and factorization density
Clpnnnn taken out of messages
If Factorization frequency default then done on size of matrix

(-)unitTest, (-)netlib or (-)netlibp will do standard tests

You can switch to interactive mode at any time so
clp watson.mps -scaling off -primalsimplex
is the same as
clp watson.mps -
scaling off
primalsimplexName,result,time,sys,elapsed,objective,continuous,tightened,cut_time,nodes,iterations,rows,columns,processed_rows,processed_columns%s,%s,%.2f,%.2f,%.2f,%.16g,%g,%g,%.2f,%d,%d,%d,%d,%d,%dAlternative solution - %d remaining (no integer solution - continuous used)**** Results not valid when LP not optimal
,increase,variable,decrease,variable
Total time (CPU seconds):       %.2f   (Wallclock seconds):       %.2f
N8CglLandP10ParametersE13CoinWarmStart7CbcUser10CbcStopNow'ÿÿÿÿ0#if defined(_MS0// Turn off compiler warning abbout long names0#  pragma warning(disable:4786)0#include <casse0#include <ioman0#include "OsiClpSolverInterface0#include "CbcMo "CbcModel.hpp"0#include "CbcCutGenerator.hpp"0#include "CbcStbcStrategy.hpp"0#include "CglPrPreProcess.hpp"0#include "CoinT "CoinTime.hpp"CglProbing.hpp"5  cbcModel->iniinitialSolve();5  if (clpModel->tightenPrimalBolBounds()!=0) {5    std::cout<<"Problem is infeasible - tightenPrimalBounds!"<<s!"<<std::endl;5  clpModel->dual();  // clean u5  // compute some things using ng problem size5  cbcModel->setMinimumDrop(CoinCoinMin(5.0e-2,5       fabs(cbcModel->getMinimizationObjValue())*1.0e-3+1.0e-4)5  if (cbcModel->getNumCols()<505    cbcModel->setMaximumCutPassesAtRoot(-100); // always do 100100 if possible5  else if (cbcModel->getNumColsNumCols()<5000)esAtRoot(100); // use minimum drssesAtRoot(20);MaximumCutPasses5  // Preprocessing using CbcStr5  CbcStrategyDefault strategy(15  strategy.setupPreProcessing(1Strategy(strateg5  // Hand codedd preprocessing5  CglPreProcessrocess process;5  OsiSolverInterface * saveSolver=cbcModel->solver()->clone();5  // Tell solver we are in Bran Branch and Cut5  saveSolver->setHintParam(OsiDoInBranchAndCut,true,OsiHintDo) 5  // Default set of cut generat5  CglProbing geing generator1;5  generator1.setUsingObjective(.setMaxPass(3);tMaxProbeRoot(saveSolver->getNum>getNumCols());tMaxElements(100tMaxLookRoot(50).setRowCuts(3);5  // Add in gen5  process.addCutGenerator(&gener(&generator1);5  process.messageHandler()->setLogLevel(cbcModel->logLevel());rface * solver2 r we are not in false,OsiHintDo)5  if (solver2)5    solver2->setHintParam(OsiDoInBranchAndCut,false,OsiHintDo) 5  if (!solver2)"Pre-processing says infeasible!e!"<<std::endl;"processed model has "<<solver2-2->getNumRows()5	     <<" rows, "<<solver2->get2->getNumCols()5	     <<" columns and "<<solver2->getNumElement5	     <<" elements"<<solver2->getNumElements()<s()<<std::endl;5  // we have to keep solver2 so2 so pass clone5  solver2 = sollver2->clone();5  cbcModel->assignSolver(solver0
int main (int argc, const charchar *argv[])
{0  OsiClpSolverInterface solver10  int status=1;0    std::cout<<"Please give file name"<<std::en0    status=solver1.readMps(argvps(argv[1],"");0  if (status) {"Bad readMps "<<argv[1]<<std::en0  double time1 = CoinCpuTime();0  CbcModel modemodel(solver1);0  // Now do requested saves andd modifications0  CbcModel * cbcModel = & model0  OsiSolverInterface * osiModel = model.solver(nterface * osiclpModel = dynamic_cast< OsiClpSolverInterface*> (e*> (osiModel);0  ClpSimplex * clpModel = osiclpModel->getModel3  // You can save some time by switching off meessage building3  // clpModel->messagesPointer()->setDetailMessages(100,10000,(,(int *) NULL);5  cbcModel->braanchAndBound();8  std::cout<<argv[1]<<" took "<<CoinCpuTime()-time1<<" seconds,8	   <<cbcModel->getNodeCount()<<" nodes with obith objective "->getObjValue()8	   <<(!cbcModel->status() ? " Finished" : " No Not finished")8	   <<std::endl5  // For best s5  int numberColumns = solver1.g1.getNumCols();>getMinimizationObjValue()<1.0e55    // post pro5    process.postProcess(*cbcModdel->solver());5    // Solution now back in sav5    cbcModel->assignSolver(saveer(saveSolver);5    memcpy(cbcModel->bestSolution(),cbcModel->solver()->getColStColSolution(),5	   numberColumns*sizeof(double5    // put back in original sol5    solver1.setColSolution(cbcM5    const double * solution = solver1.getColSoltColSolution();8  
  // Now you would use solutlution etc etc
5    // Get names from solver1 (as OsiSolverInterface may lose)5    std::vector<std::string> columnNames = *solver1.getModelPtr()->columnNames(5    int iColumnstd::setiosflags(std::ios::fixed|std::ios::showpoint)<<std::setw"--------------------------------------"<<std::e5    for (iColumn=0;iColumn<numberColumns;iColum5      double value=solution[iCo5      if (fabs(value)>1.0e-7&&solver1.isIntegereger(iColumn)) 5	std::cout<<std::setw(6)<<iColu5                 <<columnNames[s[iColumn]<<" "  <<value<<std::std::resetiosflags(std::ios::fixed|std::ios::showpoint|std::ios:s::scientific);Redundant save of default valuesSet changed valuRedundant set det default valuesRedundant restorþÿÿÿþÿÿÿþÿÿÿþÿÿÿ3eÉû-_Ã]ÃEÃÃ]ü©ñÒMbP?Y@'ýÿÿÿþÿÿÿ'ÿÿÿÿÿÿÿÿÿÿÿÿÿ¶ûÿÿüÿÿdddd@B@B@B@B'''''Welcome to the CBC MILP Solver 
command line - (default strategDouble parameterBranch and Cut double parametersInteger parameteBranch and Cut integer parameterKeyword parameteBranch and Cut keyword parameterActions or strinring parameters:Branch and Cut aOptimal solutionLinear relaxatioation infeasible found (within gn gap tolerance)xation unboundediterations limitProblem proven iroven infeasibleEnumerated nodess:           0
Total iterations** Current modelmodel not valid** Analysis indicates model infeMatrix converted to +- one matriMatrix can not be converted to + to +- 1 matrixMatrix not a ClpClpPackedMatrix to network matre converted to n network matrixProblem is infeasible - tightenPenPrimalBounds!Pre-processing says infeasible ole or unboundedEnding major passes - no solutioNo feasible solusolution found
There were errorrrors on outputerrors on inputAll alternative solutions printeò?ð=çåð>Ã@z@Ãm¶ä>Uk B{®Gázt?
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d333333Ó?¹?à?þÿÿÿ222Èè'ÿÿÿÿCutoff increment increased from %g to %g%s %s %s %sReading: %s, line %d - first column in mipstart file should be numeric, ignoring.Reading: %s, line %d - Third column in mipstart file should be numeric, ignoring.MIPStart values read for %d variables.No mipstart solution read from %sWarning: MIPstart solution is not valid, column names do not match, ignoring it.Warning: %d column names were not found (e.g. %s) while filling solution.Trying just fixing integer variables.Fixing only non-zero variables.Warning: mipstart values could not be used to build a solution.MIPStart solution provided values for %d of %d integer variables, %d variables are still fractional.Mini branch and bound defined values for remaining variables in %.2f seconds.MIPStart provided solution with cost %gReduceInMIPStart%d is possible %g <= <= %g
 (%d,el %g up %g)full%d out of %d candidate rows are possible
can't make knapsacks - did you set fixedPriority (extra1)Two nonzero values for knapsack %d at (%d,%g) and (%d,%g)
expanded column %d in knapsack %d has %d nonzero entries:
%d - original %d has value %g
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½ 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