- Ibm ilog cplex optimization studio 12.7.1 verification#
- Ibm ilog cplex optimization studio 12.7.1 code#
Model.add(IloDiffConstraint(env, Germany, Luxembourg, "test")) Model.add(IloDiffConstraint(env, France, Luxembourg, "test")) Model.add(IloDiffConstraint(env, France, Germany, "test")) Model.add(IloDiffConstraint(env, Denmark, Germany, "test")) Model.add(IloDiffConstraint(env, Belgium, Luxembourg, "test")) Model.add(IloDiffConstraint(env, Belgium, Netherlands, "test")) Model.add(IloDiffConstraint(env, Belgium, Germany, "test")) Model.add(IloDiffConstraint(env, Belgium, France, "test"))
Ibm ilog cplex optimization studio 12.7.1 code#
I took this from sample code and removed it, everything works fine now.The Graph Coloring example shipped with CP optimizer (file: color.cpp in the examples directory): #include Ĭonst char* Names = SOLVED: it was "opl.convertAllIntVars() " which converted everything to doubles.
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How can I force Java cplex to round my integers? (17.22 ticks)Įdit 2: I found that my integer values in my matrix 0.1 are not being rounded to 0 or 1 but instead are counted as 0.932. Root relaxation solution time = 0.00 sec. Parallel mode: deterministic, using up to 4 threads. MIP emphasis: balance optimality and feasibility. Reduced MIP has 226 binaries, 0 generals, 0 SOSs, and 0 indicators. Reduced MIP has 66 rows, 226 columns, and 522 nonzeros. MIP Presolve eliminated 29 rows and 30 columns. Probing fixed 6 vars, tightened 0 bounds. Reduced MIP has 256 binaries, 0 generals, 0 SOSs, and 0 indicators. Reduced MIP has 95 rows, 256 columns, and 632 nonzeros. MIP Presolve eliminated 158 rows and 306 columns. Probing fixed 8 vars, tightened 0 bounds. Reduced MIP has 562 binaries, 0 generals, 0 SOSs, and 0 indicators. Reduced MIP has 253 rows, 562 columns, and 1958 nonzeros. MIP Presolve eliminated 5027 rows and 4764 columns. It does show a lot more info about integers being cut off but this is my first experience with both programs so could someone please help me locate where the problem is or what this all means? Found incumbent of value 125.000000 after 0.00 sec. I looked at the data set and my model and an objective of less than 125 should NOT even be possible (I used extreme values to make sure that one of my objective variables was 125, so anything below that should not be possible).ĭoes anyone know why these results are different? Is it perhaps something in the settings of Java compared to IBM? Is it possible for me to import my IBM optimization studio settings to Java also to test this?ĮDIT: here are the IBM Optimization studio logs, I forgot to include those. Reduced LP has 5280 rows, 5325 columns, and 25525 nonzeros.
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LP Presolve eliminated 0 rows and 1 columns. In Java: Parallel mode: deterministic, using up to 4 threads for concurrent optimization. In IBM Optimization Studio: solution (optimal) with objective 125 Now the problem is if I run "myModel.mod" and "myData.dat" in both IBM Optimization studio and Java that I get VERY different objective results. Opl.convertAllIntVars() // converts integer bounds into LP compatible formatĭouble obj = opl.getCplex().getObjValue() IloOplDataSource dataSource = oplF.createOplDataSource(inDataFile) IloOplModel opl = oplF.createOplModel(def, cplex) IloOplModelDefinition def = oplF.createOplModelDefinition(modelSource, settings) IloOplSettings settings = oplF.createOplSettings(errHandler) IloOplModelSource modelSource = oplF.createOplModelSource("myModel.mod") IloOplErrorHandler errHandler = oplF.createOplErrorHandler(System.out) IloOplFactory oplF = new IloOplFactory() I found the quickest way to use my OLP model in Java with the following code: tDebugMode(false)
Ibm ilog cplex optimization studio 12.7.1 verification#
After verification of the model I wanted to put it in Java to script the parameters for simulation purposes. I implemented a LP problem in IBM Optimization Studio using OPL to create the model.