QLNet.Problem.gradientEvaluation C# (CSharp) Метод

        public int gradientEvaluation()
        {
            return gradientEvaluation_;
        }

        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);
                    }
                }
            }
        }