// curve optimization called here- adjust optimization parameters here internal void calculate() { FittingCost costFunction = costFunction_; Constraint constraint = new NoConstraint(); // start with the guess solution, if it exists Vector x = new Vector(size(), 0.0); if (!curve_.guessSolution_.empty()) { x = curve_.guessSolution_; } if (curve_.maxEvaluations_ == 0) { //Don't calculate, simply use given parameters to provide a fitted curve. //This turns the fittedbonddiscountcurve into an evaluator of the parametric //curve, for example allowing to use the parameters for a credit spread curve //calculated with bonds in one currency to be coupled to a discount curve in //another currency. return; } //workaround for backwards compatibility OptimizationMethod optimization = optimizationMethod_; if (optimization == null) { optimization = new Simplex(curve_.simplexLambda_); } Problem problem = new Problem(costFunction, constraint, x); double rootEpsilon = curve_.accuracy_; double functionEpsilon = curve_.accuracy_; double gradientNormEpsilon = curve_.accuracy_; EndCriteria endCriteria = new EndCriteria(curve_.maxEvaluations_, curve_.maxStationaryStateIterations_, rootEpsilon, functionEpsilon, gradientNormEpsilon); optimization.minimize(problem, endCriteria); solution_ = problem.currentValue(); numberOfIterations_ = problem.functionEvaluation(); costValue_ = problem.functionValue(); // save the results as the guess solution, in case of recalculation curve_.guessSolution_ = solution_; }
public void OptimizersTest() { //("Testing optimizers..."); setup(); // Loop over problems (currently there is only 1 problem) for (int i=0; i<costFunctions_.Count; ++i) { Problem problem = new Problem(costFunctions_[i], constraints_[i], initialValues_[i]); Vector initialValues = problem.currentValue(); // Loop over optimizers for (int j = 0; j < (optimizationMethods_[i]).Count; ++j) { double rootEpsilon = endCriterias_[i].rootEpsilon(); int endCriteriaTests = 1; // Loop over rootEpsilon for(int k=0; k<endCriteriaTests; ++k) { problem.setCurrentValue(initialValues); EndCriteria endCriteria = new EndCriteria(endCriterias_[i].maxIterations(), endCriterias_[i].maxStationaryStateIterations(), rootEpsilon, endCriterias_[i].functionEpsilon(), endCriterias_[i].gradientNormEpsilon()); rootEpsilon *= .1; EndCriteria.Type endCriteriaResult = optimizationMethods_[i][j].optimizationMethod.minimize(problem, endCriteria); Vector xMinCalculated = problem.currentValue(); Vector yMinCalculated = problem.values(xMinCalculated); // Check optimization results vs known solution if (endCriteriaResult==EndCriteria.Type.None || endCriteriaResult==EndCriteria.Type.MaxIterations || endCriteriaResult==EndCriteria.Type.Unknown) Assert.Fail("function evaluations: " + problem.functionEvaluation() + " gradient evaluations: " + problem.gradientEvaluation() + " x expected: " + xMinExpected_[i] + " x calculated: " + xMinCalculated + " x difference: " + (xMinExpected_[i]- xMinCalculated) + " rootEpsilon: " + endCriteria.rootEpsilon() + " y expected: " + yMinExpected_[i] + " y calculated: " + yMinCalculated + " y difference: " + (yMinExpected_[i]- yMinCalculated) + " functionEpsilon: " + endCriteria.functionEpsilon() + " endCriteriaResult: " + endCriteriaResult); } } } }