1 / 13

COIN-OR Open Solver Interface

COIN-OR Open Solver Interface. EURO XXI in Iceland 21st European Conference on Operational Research July 5, 2006. Open Solver Interface (OSI). What is the COIN-OR OSI? How is it used? Gotchas Summary. OSI provides a uniform interface to many LP solvers

koto
Download Presentation

COIN-OR Open Solver Interface

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. COIN-OROpen Solver Interface EURO XXI in Iceland 21st European Conference on Operational Research July 5, 2006

  2. Open Solver Interface (OSI) • What is the COIN-OR OSI? • How is it used? • Gotchas • Summary

  3. OSI provides a uniform interface to many LP solvers The goal is to isolate an application from a specific solver. An application written to the OSI should be able to easily change solvers. Clp: OsiClp CPLEX: OsiCpx DyLP: OsiDylp FortMP: OsiFmp GLPK: OsiGlpk MOSEK: OsiMsk OSL: OsiOsl SoPlex: OsiSpx SYMPHONY: OsiSym Vol: OsiVol XPRESS-MP OsiXpr What?

  4. How is it used • OSI is a C++ abstract base class • An implementation for a specific solver inherits from the OSI base class. • For example: OsiClp inherits from Osi.

  5. Example: Simple LP minimize -1x0 - 1x1 such that 1x0 + 2x1 <= 3 2x0 + 1x1 <= 3 x0 >= 0 x1 >= 0

  6. Defining LP Model – obj. & col. bounds //Define objective coefficients. minimize -1 x0 - 1 x1 int n_cols = 2; double *objective = new double[n_cols]; objective[0] = -1.0; objective[1] = -1.0; // Define column bounds // 0 <= x0 <= infinity // 0 <= x1 <= infinity double *col_lb = new double[n_cols]; //the column lower bounds double *col_ub = new double[n_cols]; //the column upper bounds col_lb[0] = 0.0; col_lb[1] = 0.0; OsiSolverInterface *si = new OsiClpSolverInterface; col_ub[0] = si->getInfinity(); col_ub[1] = si->getInfinity();

  7. Defining LP Model – rows //Define the constraint matrix. CoinPackedMatrix *matrix = new CoinPackedMatrix(false,0,0); matrix->setDimensions(0, n_cols); // -infinity <= 1 x0 + 2 x2 <= 3 CoinPackedVector row1; row1.insert(0, 1.0); row1.insert(1, 2.0); matrix->appendRow(row1); //-infinity <= 2 x0 + 1 x1 <= 3 CoinPackedVector row2; row2.insert(0, 2.0); row2.insert(1, 1.0); matrix->appendRow(row2); // Define Row Bounds int n_rows = 2; double *row_lb = new double[n_rows];//the row lower bounds double *row_ub = new double[n_rows];//the row upper bounds row_lb[0] = -1.0 * si->getInfinity(); row_ub[0] = 3.0; row_lb[1] = -1.0 * si->getInfinity(); row_ub[1] = 3.0;

  8. Pass Model to OSI, Solve, get solution //load the problem to OSI si->loadProblem(*matrix, col_lb, col_ub, objective, row_lb, row_ub); //write the MPS file to a file called example.mps si->writeMps("example"); // Solve the (relaxation of the) problem si->initialSolve(); // Check the solution if ( si->isProvenOptimal() ) { cout <<"Objective value is " <<si->getObjValue() <<endl; int nc = si->getNumCols(); const double *solution = si->getColSolution(); for( int c=0; c<nc; ++c ) { cout <<"x[" <<c <<"]=" <<solution[c] <<endl; } }

  9. Example: Cut Generation Library • Read mps file to define model • Solve lp • Generate cuts • If no new cuts generated then done • Add new cuts to model • Resolve LP • If objective function value changes continue with step 3

  10. Read MPS file and initial solve OsiClpSolverInterface si; si.readMps(p0033.mps,"mps"); // Solve continuous problem si.initialSolve(); // Instantiate cut generators CglKnapsackCover knapsackCoverCg; CglSimpleRounding simpleRoundingCg;

  11. Read MPS file and initial solve do { // Generate and apply cuts OsiCuts cuts; knapsackCoverCg.generateCuts(si,cuts); simpleRoundingCg.generateCuts(si,cuts); OsiSolverInterface::ApplyCutsReturnCode acRc = si.applyCuts(cuts,0.0); // If no cuts were applied, then done if ( acRc.getNumApplied()==0 ) break; // Resolve double obj = si.getObjValue(); // Grab obj value before resolve si.resolve(); cout <<"After applying cuts, objective value changed from " <<obj <<" to " <<si.getObjValue() <<endl; CoinRelFltEq eq(0.0001); bool equalObj = eq( si.getObjValue(), obj ); } while( !equalObj );

  12. The Reality • Osi functionality is limited compared to most LP solvers • Depending on the solver and how well the solver’s derived Osi class is written there can be performance overhead • Not all Osi’s behave the same (need better testing and definitions). • Osi provides mechanism to get to the underlying solver

  13. More info: www.coin-or.org • These charts: • www.coin-or.org/Presentations/EURO06/ • matrix, vector, floatEqual classes: • projects.coin-or.org/CoinUtils • Osi methods: • projects.coin-or.org/Osi • Cgl classes: • projects.coin-or.org/Cgl • Examples: • projects.coin-or.org/Osi/browser/trunk/Osi/examples • projects.coin-or.org/Cgl/browser/trunk/Cgl/examples

More Related