List of usage examples for org.apache.commons.math.optimization.linear SimplexSolver optimize
public RealPointValuePair optimize(final LinearObjectiveFunction f, final Collection<LinearConstraint> constraints, final GoalType goalType, final boolean restrictToNonNegative) throws OptimizationException
From source file:net.sf.tweety.math.opt.solver.ApacheCommonsSimplex.java
@Override public Map<Variable, Term> solve(ConstraintSatisfactionProblem problem) { if (!problem.isLinear()) throw new IllegalArgumentException("Simplex algorithm is for linear problems only."); //this.log.info("Wrapping optimization problem for calling the Apache Commons Simplex algorithm."); // 1.) bring all constraints in linear and normalized form Set<Statement> constraints = new HashSet<Statement>(); for (Statement s : problem) constraints.add(s.toNormalizedForm().toLinearForm()); // 2.) for every constraint we need an extra variable int numVariables = problem.getVariables().size(); // 3.) define mappings from variables to indices int index = 0; Map<Variable, Integer> origVars2Idx = new HashMap<Variable, Integer>(); for (Variable v : problem.getVariables()) origVars2Idx.put(v, index++);// w w w .j a v a2 s . c o m // 4.) Check for target function (for constraint satisfaction problems // its empty double[] coefficientsTarget = new double[numVariables]; int i = 0; for (; i < numVariables; i++) coefficientsTarget[i] = 0; double constTerm = 0; if (problem instanceof OptimizationProblem) { // bring target function in linear form Sum t = ((OptimizationProblem) problem).getTargetFunction().toLinearForm(); for (Term summand : t.getTerms()) { // as t is in linear form every summand is a product Product p = (Product) summand; if (p.getTerms().size() == 1) { // p consists of just a constant term Constant c = (Constant) p.getTerms().get(0); if (c instanceof FloatConstant) constTerm += ((FloatConstant) c).getValue(); else constTerm += ((IntegerConstant) c).getValue(); } else { // p consists of a variable and a constant Variable v = (Variable) ((p.getTerms().get(0) instanceof Variable) ? (p.getTerms().get(0)) : (p.getTerms().get(1))); Constant c = (Constant) ((p.getTerms().get(0) instanceof Constant) ? (p.getTerms().get(0)) : (p.getTerms().get(1))); double coefficient = (c instanceof FloatConstant) ? (((FloatConstant) c).getValue()) : (((IntegerConstant) c).getValue()); coefficientsTarget[origVars2Idx.get(v)] += coefficient; } } } LinearObjectiveFunction target = new LinearObjectiveFunction(coefficientsTarget, constTerm); // 5.) Represent the constraints Set<LinearConstraint> finalConstraints = new HashSet<LinearConstraint>(); for (Statement s : constraints) { double[] coefficientsConstraint = new double[numVariables]; for (i = 0; i < numVariables; i++) coefficientsConstraint[i] = 0; // as s is in linear form go through the summands Sum leftTerm = (Sum) s.getLeftTerm(); double rest = 0; for (Term summand : leftTerm.getTerms()) { // as s is in linear form every summand is a product Product p = (Product) summand; if (p.getTerms().size() == 1) { // p consists of just a constant term Constant c = (Constant) p.getTerms().get(0); if (c instanceof FloatConstant) rest += ((FloatConstant) c).getValue(); else rest += ((IntegerConstant) c).getValue(); } else { // p consists of a variable and a constant Variable v = (Variable) ((p.getTerms().get(0) instanceof Variable) ? (p.getTerms().get(0)) : (p.getTerms().get(1))); Constant c = (Constant) ((p.getTerms().get(0) instanceof Constant) ? (p.getTerms().get(0)) : (p.getTerms().get(1))); double coefficient = (c instanceof FloatConstant) ? (((FloatConstant) c).getValue()) : (((IntegerConstant) c).getValue()); coefficientsConstraint[origVars2Idx.get(v)] += coefficient; } } Relationship r = Relationship.EQ; if (s instanceof Inequation) r = Relationship.GEQ; finalConstraints.add(new LinearConstraint(coefficientsConstraint, r, -rest)); } // 6.) Optimize. try { //this.log.info("Calling the Apache Commons Simplex algorithm."); SimplexSolver solver = new SimplexSolver(0.01); solver.setMaxIterations(this.MAXITERATIONS); RealPointValuePair r = null; if (problem instanceof OptimizationProblem) { int type = ((OptimizationProblem) problem).getType(); r = solver.optimize(target, finalConstraints, (type == OptimizationProblem.MINIMIZE) ? (GoalType.MINIMIZE) : (GoalType.MAXIMIZE), this.onlyPositive); } else r = solver.optimize(target, finalConstraints, GoalType.MINIMIZE, this.onlyPositive); //this.log.info("Parsing output from the Apache Commons Simplex algorithm."); Map<Variable, Term> result = new HashMap<Variable, Term>(); for (Variable v : origVars2Idx.keySet()) result.put(v, new FloatConstant(r.getPoint()[origVars2Idx.get(v)])); return result; } catch (OptimizationException e) { //log.error(e.getMessage()); throw new ProblemInconsistentException(); } }
From source file:emlab.role.AbstractEnergyProducerRole.java
/** * The fuel mix is calculated with a linear optimization model of the possible fuels and the requirements. * //from w w w . java 2 s. c om * @param substancePriceMap * contains the possible fuels and their market prices * @param minimumFuelMixQuality * is the minimum fuel quality needed for the power plant to work * @param efficiency * of the plant determines the need for fuel per MWhe * @param co2TaxLevel * is part of the cost for CO2 * @param co2AuctionPrice * is part of the cost for CO2 * @return the fuel mix */ public Set<SubstanceShareInFuelMix> calculateFuelMix(PowerPlant plant, Map<Substance, Double> substancePriceMap, double co2Price) { double efficiency = plant.getActualEfficiency(); Set<SubstanceShareInFuelMix> fuelMix = (plant.getFuelMix() == null) ? new HashSet<SubstanceShareInFuelMix>() : plant.getFuelMix(); int numberOfFuels = substancePriceMap.size(); if (numberOfFuels == 0) { logger.info("No fuels, so no operation mode is set. Empty fuel mix is returned"); return new HashSet<SubstanceShareInFuelMix>(); } else if (numberOfFuels == 1) { SubstanceShareInFuelMix ssifm = null; if (!fuelMix.isEmpty()) { ssifm = fuelMix.iterator().next(); } else { ssifm = new SubstanceShareInFuelMix().persist(); fuelMix.add(ssifm); } Substance substance = substancePriceMap.keySet().iterator().next(); ssifm.setShare(calculateFuelConsumptionWhenOnlyOneFuelIsUsed(substance, efficiency)); ssifm.setSubstance(substance); logger.info("Setting fuel consumption for {} to {}", ssifm.getSubstance().getName(), ssifm.getShare()); return fuelMix; } else { double minimumFuelMixQuality = plant.getTechnology().getMinimumFuelQuality(); double[] fuelAndCO2Costs = new double[numberOfFuels]; double[] fuelDensities = new double[numberOfFuels]; double[] fuelQuality = new double[numberOfFuels]; int i = 0; for (Substance substance : substancePriceMap.keySet()) { fuelAndCO2Costs[i] = substancePriceMap.get(substance) + substance.getCo2Density() * (co2Price); fuelDensities[i] = substance.getEnergyDensity(); fuelQuality[i] = substance.getQuality() - minimumFuelMixQuality; i++; } logger.info("Fuel prices: {}", fuelAndCO2Costs); logger.info("Fuel densities: {}", fuelDensities); logger.info("Fuel purities: {}", fuelQuality); // Objective function = minimize fuel cost (fuel // consumption*fuelprices // + CO2 intensity*co2 price/tax) LinearObjectiveFunction function = new LinearObjectiveFunction(fuelAndCO2Costs, 0d); List<LinearConstraint> constraints = new ArrayList<LinearConstraint>(); // Constraint 1: total fuel density * fuel consumption should match // required energy input constraints.add(new LinearConstraint(fuelDensities, Relationship.EQ, (1 / efficiency))); // Constraint 2&3: minimum fuel quality (times fuel consumption) // required // The equation is derived from (example for 2 fuels): q1 * x1 / (x1+x2) + q2 * x2 / (x1+x2) >= qmin // so that the fuelquality weighted by the mass percentages is greater than the minimum fuel quality. constraints.add(new LinearConstraint(fuelQuality, Relationship.GEQ, 0)); try { SimplexSolver solver = new SimplexSolver(); RealPointValuePair solution = solver.optimize(function, constraints, GoalType.MINIMIZE, true); logger.info("Succesfully solved a linear optimization for fuel mix"); int f = 0; Iterator<SubstanceShareInFuelMix> iterator = plant.getFuelMix().iterator(); for (Substance substance : substancePriceMap.keySet()) { double share = solution.getPoint()[f]; SubstanceShareInFuelMix ssifm; if (iterator.hasNext()) { ssifm = iterator.next(); } else { ssifm = new SubstanceShareInFuelMix().persist(); fuelMix.add(ssifm); } double fuelConsumptionPerMWhElectricityProduced = convertFuelShareToMassVolume(share); logger.info("Setting fuel consumption for {} to {}", substance.getName(), fuelConsumptionPerMWhElectricityProduced); ssifm.setShare(fuelConsumptionPerMWhElectricityProduced); ssifm.setSubstance(substance); f++; } logger.info("If single fired, it would have been: {}", calculateFuelConsumptionWhenOnlyOneFuelIsUsed(substancePriceMap.keySet().iterator().next(), efficiency)); return fuelMix; } catch (OptimizationException e) { logger.warn( "Failed to determine the correct fuel mix. Adding only fuel number 1 in fuel mix out of {} substances and minimum quality of {}", substancePriceMap.size(), minimumFuelMixQuality); logger.info("The fuel added is: {}", substancePriceMap.keySet().iterator().next().getName()); // Override the old one fuelMix = new HashSet<SubstanceShareInFuelMix>(); SubstanceShareInFuelMix ssifm = new SubstanceShareInFuelMix().persist(); Substance substance = substancePriceMap.keySet().iterator().next(); ssifm.setShare(calculateFuelConsumptionWhenOnlyOneFuelIsUsed(substance, efficiency)); ssifm.setSubstance(substance); logger.info("Setting fuel consumption for {} to {}", ssifm.getSubstance().getName(), ssifm.getShare()); fuelMix.add(ssifm); return fuelMix; } } }
From source file:org.rascalmpl.library.analysis.linearprogramming.LinearProgramming.java
public IValue llOptimize(IBool minimize, IBool nonNegative, ISet constraints, IConstructor f) { SimplexSolver solver = new SimplexSolver(); ArrayList<LinearConstraint> constraintsJ = new ArrayList<LinearConstraint>(constraints.size()); for (IValue v : constraints) { constraintsJ.add(convertConstraint((IConstructor) v)); }//from w ww .j a va2s.co m LinearObjectiveFunction fJ = convertLinObjFun(f); GoalType goal = minimize.getValue() ? GoalType.MINIMIZE : GoalType.MAXIMIZE; IValueFactory vf = values; boolean nonNegativeJ = nonNegative.getValue(); try { RealPointValuePair res = solver.optimize(fJ, constraintsJ, goal, nonNegativeJ); return vf.constructor(Maybe.Maybe_just, vf.constructor(LLSolution_llSolution, convertToRealList(res.getPoint(), vf), vf.real(res.getValue()))); } catch (Exception e) { return vf.constructor(Maybe.Maybe_nothing); } }
From source file:rb.app.RBnSCMCSystem.java
public double get_SCM_LB() { //return 0.01; double min_J_obj = 0.; double[] min_Jlocal_obj = new double[n_mubar]; // Sort the indices of mu_bar based on distance from current_parameters List<Integer> sortedmubarIndices = getSorted_CJ_Indices(mu_bar); int icount = 0; //while ((min_J_obj<=0) && (icount < sortedmubarIndices.size())){ while ((min_J_obj <= 0) && (icount < sortedmubarIndices.size())) { int imubar = sortedmubarIndices.get(icount); // First, declare the constraints Collection constraints = new ArrayList(); // Add bounding box constraints for the get_Q_a() variables for (int q = 0; q < get_Q_a(); q++) { double[] index = new double[get_Q_a() * 2]; index[q] = 1.;/*from w w w. j ava 2 s . c o m*/ constraints.add(new LinearConstraint(index, Relationship.GEQ, B_min[q] / beta_bar[imubar])); constraints.add(new LinearConstraint(index, Relationship.LEQ, B_max[q] / beta_bar[imubar])); index[q] = 0.; index[q + get_Q_a()] = 1.; constraints.add(new LinearConstraint(index, Relationship.GEQ, B_min[q] / beta_bar[imubar])); constraints.add(new LinearConstraint(index, Relationship.LEQ, B_max[q] / beta_bar[imubar])); } // Save the current_parameters since we'll change them in the loop below save_current_parameters(); // Add the constraint rows if (n_muhat[imubar] > 0) { for (int imuhat = 0; imuhat < n_muhat[imubar]; imuhat++) { current_parameters = mu_hat[imubar].get(imuhat); double[] constraint_row = new double[get_Q_a() * 2]; for (int q = 0; q < get_Q_a(); q++) { Complex theta_q_a = complex_eval_theta_q_a(q); constraint_row[q] = theta_q_a.getReal() * beta_bar[imubar]; constraint_row[q + get_Q_a()] = theta_q_a.getImaginary() * beta_bar[imubar]; } constraints .add(new LinearConstraint(constraint_row, Relationship.GEQ, beta_hat[imubar][imuhat])); } } // Now load the original parameters back into current_parameters // in order to set the coefficients of the objective function reload_current_parameters(); // Create objective function object double[] objectiveFn = new double[get_Q_a() * 2]; for (int q = 0; q < get_Q_a(); q++) { Complex theta_q_a = complex_eval_theta_q_a(q); objectiveFn[q] = theta_q_a.getReal() * beta_bar[imubar]; objectiveFn[q + get_Q_a()] = theta_q_a.getImaginary() * beta_bar[imubar]; } LinearObjectiveFunction f = new LinearObjectiveFunction(objectiveFn, 0.); try { SimplexSolver solver = new SimplexSolver(1e-6); RealPointValuePair opt_pair = solver.optimize(f, constraints, GoalType.MINIMIZE, false); min_Jlocal_obj[icount] = opt_pair.getValue(); } catch (OptimizationException e) { Log.e("RBSCMSYSTEM_TAG", "Optimal solution not found"); e.printStackTrace(); } catch (Exception e) { Log.e("RBSCMSYSTEM_TAG", "Exception occurred during SCM_LB calculation"); e.printStackTrace(); } min_J_obj = min_J_obj > min_Jlocal_obj[icount] ? min_J_obj : min_Jlocal_obj[icount]; icount++; } return min_J_obj; }
From source file:rb.app.RBSCMSystem.java
/** * @return the SCM lower bound for the current parameters. *///from www.ja va 2 s . c o m public double get_SCM_LB() { double min_J_obj = 0.; try { // First, declare the constraints Collection constraints = new ArrayList(); // Add bounding box constraints for the get_Q_a() variables for (int q = 0; q < get_Q_a(); q++) { double[] index = new double[get_Q_a()]; index[q] = 1.; constraints.add(new LinearConstraint(index, Relationship.GEQ, B_min[q])); constraints.add(new LinearConstraint(index, Relationship.LEQ, B_max[q])); } // Sort the indices of C_J based on distance from current_parameters List<Integer> sortedIndices = getSorted_CJ_Indices(); // Save the current_parameters since we'll change them in the loop below save_current_parameters(); // Add the constraint rows int n_rows = Math.min(SCM_M, C_J.size()); int count = 1; if (n_rows > 0) { for (Iterator it = sortedIndices.iterator(); it.hasNext();) { Integer mu_index = (Integer) it.next(); get_current_parameters_from_C_J(mu_index); // current_parameters = C_J.get(mu_index); double[] constraint_row = new double[get_Q_a()]; for (int q = 0; q < get_Q_a(); q++) { constraint_row[q] = eval_theta_q_a(q); } constraints.add( new LinearConstraint(constraint_row, Relationship.GEQ, C_J_stability_vector[mu_index])); if (count >= n_rows) break; count++; } } // Now load the original parameters back into current_parameters // in order to set the coefficients of the objective function reload_current_parameters(); // Create objective function object double[] objectiveFn = new double[get_Q_a()]; for (int q = 0; q < get_Q_a(); q++) { objectiveFn[q] = eval_theta_q_a(q); } LinearObjectiveFunction f = new LinearObjectiveFunction(objectiveFn, 0.); SimplexSolver solver = new SimplexSolver(); RealPointValuePair opt_pair = solver.optimize(f, constraints, GoalType.MINIMIZE, false); min_J_obj = opt_pair.getValue(); } catch (OptimizationException e) { Log.e("DEBUG_TAG", "Optimal solution not found"); e.printStackTrace(); } catch (Exception e) { Log.e("DEBUG_TAG", "Exception occurred during SCM_LB calculation"); e.printStackTrace(); } Log.d(DEBUG_TAG, "SCM val = " + min_J_obj); return min_J_obj; }