/** Simple constructor with default settings. * <p>The convergence check is set to a {@link SimpleScalarValueChecker} * and the maximal number of evaluation is set to its default value.</p> */ protected AbstractScalarDifferentiableOptimizer() { setConvergenceChecker(new SimpleScalarValueChecker()); setMaxIterations(DEFAULT_MAX_ITERATIONS); setMaxEvaluations(Integer.MAX_VALUE); }
/** {@inheritDoc} */ public RealPointValuePair optimize(final DifferentiableMultivariateRealFunction f, final GoalType goalType, final double[] startPoint) throws FunctionEvaluationException, OptimizationException, IllegalArgumentException { // reset counters iterations = 0; evaluations = 0; gradientEvaluations = 0; // store optimization problem characteristics function = f; gradient = f.gradient(); goal = goalType; point = startPoint.clone(); return doOptimize(); }
/** {@inheritDoc} */ public RealPointValuePair optimize(final DifferentiableMultivariateRealFunction f, final GoalType goalType, final double[] startPoint) throws FunctionEvaluationException, OptimizationException, IllegalArgumentException { // reset counters iterations = 0; evaluations = 0; gradientEvaluations = 0; // store optimization problem characteristics function = f; gradient = f.gradient(); goal = goalType; point = startPoint.clone(); return doOptimize(); }
/** Simple constructor with default settings. * <p>The convergence check is set to a {@link SimpleScalarValueChecker} * and the maximal number of evaluation is set to its default value.</p> */ protected AbstractScalarDifferentiableOptimizer() { setConvergenceChecker(new SimpleScalarValueChecker()); setMaxIterations(DEFAULT_MAX_ITERATIONS); setMaxEvaluations(Integer.MAX_VALUE); }