import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
/**
 * Optimize an objective function.
 *
 * @param maxEval Allowed number of evaluations of the objective function.
 * @param f Objective function.
 * @param goalType Optimization type.
 * @param optData Optimization data. The following data will be looked for:
 * <ul>
 *  <li>{@link InitialGuess}</li>
 *  <li>{@link SimpleBounds}</li>
 * </ul>
 * @return the point/value pair giving the optimal value of the objective
 * function.
 * @throws TooManyEvaluationsException if the maximal number of
 * evaluations is exceeded.
 * @since 3.1
 */
protected PointValuePair optimizeInternal(int maxEval,
                                          FUNC f,
                                          GoalType goalType,
                                          OptimizationData... optData)
    throws TooManyEvaluationsException {
    // Set internal state.
    evaluations.setMaximalCount(maxEval);
    evaluations.resetCount();
    function = f;
    goal = goalType;
    // Retrieve other settings.
    parseOptimizationData(optData);
    // Check input consistency.
    checkParameters();
    // Perform computation.
    return doOptimize();
}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
/** {@inheritDoc} */
@Override
protected PointValuePair doOptimize() {
    final double[] lowerBound = getLowerBound();
    final double[] upperBound = getUpperBound();

    // Validity checks.
    setup(lowerBound, upperBound);

    isMinimize = (getGoalType() == GoalType.MINIMIZE);
    currentBest = new ArrayRealVector(getStartPoint());

    final double value = bobyqa(lowerBound, upperBound);

    return new PointValuePair(currentBest.getDataRef(),
                                  isMinimize ? value : -value);
}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
/**
 * Build an initial simplex.
 *
 * @param startPoint First point of the simplex.
 * @throws DimensionMismatchException if the start point does not match
 * simplex dimension.
 */
public void build(final double[] startPoint) {
    if (dimension != startPoint.length) {
        throw new DimensionMismatchException(dimension, startPoint.length);
    }

    // Set first vertex.
    simplex = new PointValuePair[dimension + 1];
    simplex[0] = new PointValuePair(startPoint, Double.NaN);

    // Set remaining vertices.
    for (int i = 0; i < dimension; i++) {
        final double[] confI = startConfiguration[i];
        final double[] vertexI = new double[dimension];
        for (int k = 0; k < dimension; k++) {
            vertexI[k] = startPoint[k] + confI[k];
        }
        simplex[i + 1] = new PointValuePair(vertexI, Double.NaN);
    }
}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
/**
 * Compute and evaluate a new simplex.
 *
 * @param evaluationFunction Evaluation function.
 * @param original Original simplex (to be preserved).
 * @param coeff Linear coefficient.
 * @param comparator Comparator to use to sort simplex vertices from best
 * to poorest.
 * @return the best point in the transformed simplex.
 * @throws org.apache.commons.math3.exception.TooManyEvaluationsException
 * if the maximal number of evaluations is exceeded.
 */
private PointValuePair evaluateNewSimplex(final MultivariateFunction evaluationFunction,
                                              final PointValuePair[] original,
                                              final double coeff,
                                              final Comparator<PointValuePair> comparator) {
    final double[] xSmallest = original[0].getPointRef();
    // Perform a linear transformation on all the simplex points,
    // except the first one.
    setPoint(0, original[0]);
    final int dim = getDimension();
    for (int i = 1; i < getSize(); i++) {
        final double[] xOriginal = original[i].getPointRef();
        final double[] xTransformed = new double[dim];
        for (int j = 0; j < dim; j++) {
            xTransformed[j] = xSmallest[j] + coeff * (xSmallest[j] - xOriginal[j]);
        }
        setPoint(i, new PointValuePair(xTransformed, Double.NaN, false));
    }

    // Evaluate the simplex.
    evaluate(evaluationFunction, comparator);

    return getPoint(0);
}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
/**
 * This constructor allows to specify a user-defined convergence checker,
 * in addition to the parameters that control the default convergence
 * checking procedure and the line search tolerances.
 *
 * @param rel Relative threshold for this optimizer.
 * @param abs Absolute threshold for this optimizer.
 * @param lineRel Relative threshold for the internal line search optimizer.
 * @param lineAbs Absolute threshold for the internal line search optimizer.
 * @param checker Convergence checker.
 * @throws NotStrictlyPositiveException if {@code abs <= 0}.
 * @throws NumberIsTooSmallException if {@code rel < 2 * Math.ulp(1d)}.
 */
public PowellOptimizer(double rel,
                       double abs,
                       double lineRel,
                       double lineAbs,
                       ConvergenceChecker<PointValuePair> checker) {
    super(checker);

    if (rel < MIN_RELATIVE_TOLERANCE) {
        throw new NumberIsTooSmallException(rel, MIN_RELATIVE_TOLERANCE, true);
    }
    if (abs <= 0) {
        throw new NotStrictlyPositiveException(abs);
    }
    relativeThreshold = rel;
    absoluteThreshold = abs;

    // Create the line search optimizer.
    line = new LineSearch(lineRel,
                          lineAbs);
}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
/**
 * @param maxIterations Maximal number of iterations.
 * @param stopFitness Whether to stop if objective function value is smaller than
 * {@code stopFitness}.
 * @param isActiveCMA Chooses the covariance matrix update method.
 * @param diagonalOnly Number of initial iterations, where the covariance matrix
 * remains diagonal.
 * @param checkFeasableCount Determines how often new random objective variables are
 * generated in case they are out of bounds.
 * @param random Random generator.
 * @param generateStatistics Whether statistic data is collected.
 * @param checker Convergence checker.
 *
 * @since 3.1
 */
public CMAESOptimizer(int maxIterations,
                      double stopFitness,
                      boolean isActiveCMA,
                      int diagonalOnly,
                      int checkFeasableCount,
                      RandomGenerator random,
                      boolean generateStatistics,
                      ConvergenceChecker<PointValuePair> checker) {
    super(checker);
    this.maxIterations = maxIterations;
    this.stopFitness = stopFitness;
    this.isActiveCMA = isActiveCMA;
    this.diagonalOnly = diagonalOnly;
    this.checkFeasableCount = checkFeasableCount;
    this.random = random;
    this.generateStatistics = generateStatistics;
}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
/** {@inheritDoc} */
public PointValuePair optimize(final LinearObjectiveFunction f,
                               final Collection<LinearConstraint> constraints,
                               final GoalType goalType, final boolean restrictToNonNegative)
    throws MathIllegalStateException {

    // store linear problem characteristics
    this.function          = f;
    this.linearConstraints = constraints;
    this.goal              = goalType;
    this.nonNegative       = restrictToNonNegative;

    iterations  = 0;

    // solve the problem
    return doOptimize();

}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
/** {@inheritDoc} */
@Override
public PointValuePair doOptimize()
    throws MaxCountExceededException, UnboundedSolutionException, NoFeasibleSolutionException {
    final SimplexTableau tableau =
        new SimplexTableau(getFunction(),
                           getConstraints(),
                           getGoalType(),
                           restrictToNonNegative(),
                           epsilon,
                           maxUlps);

    solvePhase1(tableau);
    tableau.dropPhase1Objective();

    while (!tableau.isOptimal()) {
        doIteration(tableau);
    }
    return tableau.getSolution();
}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
@Ignore @Test
public void testConstrainedRosenWithMoreInterpolationPoints() {
    final double[] startPoint = point(DIM, 0.1);
    final double[][] boundaries = boundaries(DIM, -1, 2);
    final PointValuePair expected = new PointValuePair(point(DIM, 1.0), 0.0);

    // This should have been 78 because in the code the hard limit is
    // said to be
    //   ((DIM + 1) * (DIM + 2)) / 2 - (2 * DIM + 1)
    // i.e. 78 in this case, but the test fails for 48, 59, 62, 63, 64,
    // 65, 66, ...
    final int maxAdditionalPoints = 47;

    for (int num = 1; num <= maxAdditionalPoints; num++) {
        doTest(new Rosen(), startPoint, boundaries,
               GoalType.MINIMIZE,
               1e-12, 1e-6, 2000,
               num,
               expected,
               "num=" + num);
    }
}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
@Test
public void testUnbounded() {

    final BiQuadratic biQuadratic = new BiQuadratic(4.0, 0.0,
                                                    Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY,
                                                    Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY);
    final MultivariateFunctionPenaltyAdapter wrapped =
            new MultivariateFunctionPenaltyAdapter(biQuadratic,
                                                       biQuadratic.getLower(),
                                                       biQuadratic.getUpper(),
                                                       1000.0, new double[] { 100.0, 100.0 });

    SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
    optimizer.setSimplex(new NelderMeadSimplex(new double[] { 1.0, 0.5 }));

    final PointValuePair optimum
        = optimizer.optimize(300, wrapped, GoalType.MINIMIZE, new double[] { -1.5, 4.0 });

    Assert.assertEquals(biQuadratic.getBoundedXOptimum(), optimum.getPoint()[0], 2e-7);
    Assert.assertEquals(biQuadratic.getBoundedYOptimum(), optimum.getPoint()[1], 2e-7);

}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
@Test
public void testMath828() {
    LinearObjectiveFunction f = new LinearObjectiveFunction(
            new double[] { 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, 0.0);
    
    ArrayList <LinearConstraint>constraints = new ArrayList<LinearConstraint>();

    constraints.add(new LinearConstraint(new double[] {0.0, 39.0, 23.0, 96.0, 15.0, 48.0, 9.0, 21.0, 48.0, 36.0, 76.0, 19.0, 88.0, 17.0, 16.0, 36.0,}, Relationship.GEQ, 15.0));
    constraints.add(new LinearConstraint(new double[] {0.0, 59.0, 93.0, 12.0, 29.0, 78.0, 73.0, 87.0, 32.0, 70.0, 68.0, 24.0, 11.0, 26.0, 65.0, 25.0,}, Relationship.GEQ, 29.0));
    constraints.add(new LinearConstraint(new double[] {0.0, 74.0, 5.0, 82.0, 6.0, 97.0, 55.0, 44.0, 52.0, 54.0, 5.0, 93.0, 91.0, 8.0, 20.0, 97.0,}, Relationship.GEQ, 6.0));
    constraints.add(new LinearConstraint(new double[] {8.0, -3.0, -28.0, -72.0, -8.0, -31.0, -31.0, -74.0, -47.0, -59.0, -24.0, -57.0, -56.0, -16.0, -92.0, -59.0,}, Relationship.GEQ, 0.0));
    constraints.add(new LinearConstraint(new double[] {25.0, -7.0, -99.0, -78.0, -25.0, -14.0, -16.0, -89.0, -39.0, -56.0, -53.0, -9.0, -18.0, -26.0, -11.0, -61.0,}, Relationship.GEQ, 0.0));
    constraints.add(new LinearConstraint(new double[] {33.0, -95.0, -15.0, -4.0, -33.0, -3.0, -20.0, -96.0, -27.0, -13.0, -80.0, -24.0, -3.0, -13.0, -57.0, -76.0,}, Relationship.GEQ, 0.0));
    constraints.add(new LinearConstraint(new double[] {7.0, -95.0, -39.0, -93.0, -7.0, -94.0, -94.0, -62.0, -76.0, -26.0, -53.0, -57.0, -31.0, -76.0, -53.0, -52.0,}, Relationship.GEQ, 0.0));
    
    double epsilon = 1e-6;
    PointValuePair solution = new SimplexSolver().optimize(f, constraints, GoalType.MINIMIZE, true);
    Assert.assertEquals(1.0d, solution.getValue(), epsilon);
    Assert.assertTrue(validSolution(solution, constraints, epsilon));
}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
@Test
public void testMath272() {
    LinearObjectiveFunction f = new LinearObjectiveFunction(new double[] { 2, 2, 1 }, 0);
    Collection<LinearConstraint> constraints = new ArrayList<LinearConstraint>();
    constraints.add(new LinearConstraint(new double[] { 1, 1, 0 }, Relationship.GEQ,  1));
    constraints.add(new LinearConstraint(new double[] { 1, 0, 1 }, Relationship.GEQ,  1));
    constraints.add(new LinearConstraint(new double[] { 0, 1, 0 }, Relationship.GEQ,  1));

    SimplexSolver solver = new SimplexSolver();
    PointValuePair solution = solver.optimize(f, constraints, GoalType.MINIMIZE, true);

    Assert.assertEquals(0.0, solution.getPoint()[0], .0000001);
    Assert.assertEquals(1.0, solution.getPoint()[1], .0000001);
    Assert.assertEquals(1.0, solution.getPoint()[2], .0000001);
    Assert.assertEquals(3.0, solution.getValue(), .0000001);
}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
@Test
public void testMath828Cycle() {
    LinearObjectiveFunction f = new LinearObjectiveFunction(
            new double[] { 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}, 0.0);
    
    ArrayList <LinearConstraint>constraints = new ArrayList<LinearConstraint>();

    constraints.add(new LinearConstraint(new double[] {0.0, 16.0, 14.0, 69.0, 1.0, 85.0, 52.0, 43.0, 64.0, 97.0, 14.0, 74.0, 89.0, 28.0, 94.0, 58.0, 13.0, 22.0, 21.0, 17.0, 30.0, 25.0, 1.0, 59.0, 91.0, 78.0, 12.0, 74.0, 56.0, 3.0, 88.0,}, Relationship.GEQ, 91.0));
    constraints.add(new LinearConstraint(new double[] {0.0, 60.0, 40.0, 81.0, 71.0, 72.0, 46.0, 45.0, 38.0, 48.0, 40.0, 17.0, 33.0, 85.0, 64.0, 32.0, 84.0, 3.0, 54.0, 44.0, 71.0, 67.0, 90.0, 95.0, 54.0, 99.0, 99.0, 29.0, 52.0, 98.0, 9.0,}, Relationship.GEQ, 54.0));
    constraints.add(new LinearConstraint(new double[] {0.0, 41.0, 12.0, 86.0, 90.0, 61.0, 31.0, 41.0, 23.0, 89.0, 17.0, 74.0, 44.0, 27.0, 16.0, 47.0, 80.0, 32.0, 11.0, 56.0, 68.0, 82.0, 11.0, 62.0, 62.0, 53.0, 39.0, 16.0, 48.0, 1.0, 63.0,}, Relationship.GEQ, 62.0));
    constraints.add(new LinearConstraint(new double[] {83.0, -76.0, -94.0, -19.0, -15.0, -70.0, -72.0, -57.0, -63.0, -65.0, -22.0, -94.0, -22.0, -88.0, -86.0, -89.0, -72.0, -16.0, -80.0, -49.0, -70.0, -93.0, -95.0, -17.0, -83.0, -97.0, -31.0, -47.0, -31.0, -13.0, -23.0,}, Relationship.GEQ, 0.0));
    constraints.add(new LinearConstraint(new double[] {41.0, -96.0, -41.0, -48.0, -70.0, -43.0, -43.0, -43.0, -97.0, -37.0, -85.0, -70.0, -45.0, -67.0, -87.0, -69.0, -94.0, -54.0, -54.0, -92.0, -79.0, -10.0, -35.0, -20.0, -41.0, -41.0, -65.0, -25.0, -12.0, -8.0, -46.0,}, Relationship.GEQ, 0.0));
    constraints.add(new LinearConstraint(new double[] {27.0, -42.0, -65.0, -49.0, -53.0, -42.0, -17.0, -2.0, -61.0, -31.0, -76.0, -47.0, -8.0, -93.0, -86.0, -62.0, -65.0, -63.0, -22.0, -43.0, -27.0, -23.0, -32.0, -74.0, -27.0, -63.0, -47.0, -78.0, -29.0, -95.0, -73.0,}, Relationship.GEQ, 0.0));
    constraints.add(new LinearConstraint(new double[] {15.0, -46.0, -41.0, -83.0, -98.0, -99.0, -21.0, -35.0, -7.0, -14.0, -80.0, -63.0, -18.0, -42.0, -5.0, -34.0, -56.0, -70.0, -16.0, -18.0, -74.0, -61.0, -47.0, -41.0, -15.0, -79.0, -18.0, -47.0, -88.0, -68.0, -55.0,}, Relationship.GEQ, 0.0));
    
    double epsilon = 1e-6;
    PointValuePair solution = new SimplexSolver().optimize(f, constraints, GoalType.MINIMIZE, true);
    Assert.assertEquals(1.0d, solution.getValue(), epsilon);
    Assert.assertTrue(validSolution(solution, constraints, epsilon));        
}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
@Test
public void testRestrictVariablesToNonNegative() {
    LinearObjectiveFunction f = new LinearObjectiveFunction(new double[] { 409, 523, 70, 204, 339 }, 0);
    Collection<LinearConstraint> constraints = new ArrayList<LinearConstraint>();
    constraints.add(new LinearConstraint(new double[] {    43,   56, 345,  56,    5 }, Relationship.LEQ,  4567456));
    constraints.add(new LinearConstraint(new double[] {    12,   45,   7,  56,   23 }, Relationship.LEQ,    56454));
    constraints.add(new LinearConstraint(new double[] {     8,  768,   0,  34, 7456 }, Relationship.LEQ,  1923421));
    constraints.add(new LinearConstraint(new double[] { 12342, 2342,  34, 678, 2342 }, Relationship.GEQ,     4356));
    constraints.add(new LinearConstraint(new double[] {    45,  678,  76,  52,   23 }, Relationship.EQ,    456356));

    SimplexSolver solver = new SimplexSolver();
    PointValuePair solution = solver.optimize(f, constraints, GoalType.MAXIMIZE, true);
    Assert.assertEquals(2902.92783505155, solution.getPoint()[0], .0000001);
    Assert.assertEquals(480.419243986254, solution.getPoint()[1], .0000001);
    Assert.assertEquals(0.0, solution.getPoint()[2], .0000001);
    Assert.assertEquals(0.0, solution.getPoint()[3], .0000001);
    Assert.assertEquals(0.0, solution.getPoint()[4], .0000001);
    Assert.assertEquals(1438556.7491409, solution.getValue(), .0000001);
}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
@Test
public void testLeastSquares2() {

    final RealMatrix factors =
        new Array2DRowRealMatrix(new double[][] {
                { 1, 0 },
                { 0, 1 }
            }, false);
    LeastSquaresConverter ls = new LeastSquaresConverter(new MultivariateVectorFunction() {
            public double[] value(double[] variables) {
                return factors.operate(variables);
            }
        }, new double[] { 2, -3 }, new double[] { 10, 0.1 });
    SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-6);
    optimizer.setSimplex(new NelderMeadSimplex(2));
    PointValuePair optimum =
        optimizer.optimize(200, ls, GoalType.MINIMIZE, new double[] { 10, 10 });
    Assert.assertEquals( 2, optimum.getPointRef()[0], 5e-5);
    Assert.assertEquals(-3, optimum.getPointRef()[1], 8e-4);
    Assert.assertTrue(optimizer.getEvaluations() > 60);
    Assert.assertTrue(optimizer.getEvaluations() < 80);
    Assert.assertTrue(optimum.getValue() < 1e-6);
}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
@Test
  public void testEpsilon() {
    LinearObjectiveFunction f =
        new LinearObjectiveFunction(new double[] { 10, 5, 1 }, 0);
    Collection<LinearConstraint> constraints = new ArrayList<LinearConstraint>();
    constraints.add(new LinearConstraint(new double[] {  9, 8, 0 }, Relationship.EQ,  17));
    constraints.add(new LinearConstraint(new double[] {  0, 7, 8 }, Relationship.LEQ,  7));
    constraints.add(new LinearConstraint(new double[] { 10, 0, 2 }, Relationship.LEQ, 10));

    SimplexSolver solver = new SimplexSolver();
    PointValuePair solution = solver.optimize(f, constraints, GoalType.MAXIMIZE, false);
    Assert.assertEquals(1.0, solution.getPoint()[0], 0.0);
    Assert.assertEquals(1.0, solution.getPoint()[1], 0.0);
    Assert.assertEquals(0.0, solution.getPoint()[2], 0.0);
    Assert.assertEquals(15.0, solution.getValue(), 0.0);
}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
@Test
public void testColumnsPermutation() {
    LinearProblem problem =
        new LinearProblem(new double[][] { { 1.0, -1.0 }, { 0.0, 2.0 }, { 1.0, -2.0 } },
                          new double[] { 4.0, 6.0, 1.0 });

    NonLinearConjugateGradientOptimizer optimizer =
        new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                new SimpleValueChecker(1e-6, 1e-6));
    PointValuePair optimum =
        optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0, 0 });
    Assert.assertEquals(7.0, optimum.getPoint()[0], 1.0e-10);
    Assert.assertEquals(3.0, optimum.getPoint()[1], 1.0e-10);
    Assert.assertEquals(0.0, optimum.getValue(), 1.0e-10);

}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
@Test
public void testNoDependency() {
    LinearProblem problem = new LinearProblem(new double[][] {
            { 2, 0, 0, 0, 0, 0 },
            { 0, 2, 0, 0, 0, 0 },
            { 0, 0, 2, 0, 0, 0 },
            { 0, 0, 0, 2, 0, 0 },
            { 0, 0, 0, 0, 2, 0 },
            { 0, 0, 0, 0, 0, 2 }
    }, new double[] { 0.0, 1.1, 2.2, 3.3, 4.4, 5.5 });
    NonLinearConjugateGradientOptimizer optimizer =
        new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                new SimpleValueChecker(1e-6, 1e-6));
    PointValuePair optimum =
        optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0, 0, 0, 0, 0, 0 });
    for (int i = 0; i < problem.target.length; ++i) {
        Assert.assertEquals(0.55 * i, optimum.getPoint()[i], 1.0e-10);
    }
}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
@Test
public void testOneSet() {
    LinearProblem problem = new LinearProblem(new double[][] {
            {  1,  0, 0 },
            { -1,  1, 0 },
            {  0, -1, 1 }
    }, new double[] { 1, 1, 1});
    NonLinearConjugateGradientOptimizer optimizer =
        new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                new SimpleValueChecker(1e-6, 1e-6));
    PointValuePair optimum =
        optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0, 0, 0 });
    Assert.assertEquals(1.0, optimum.getPoint()[0], 1.0e-10);
    Assert.assertEquals(2.0, optimum.getPoint()[1], 1.0e-10);
    Assert.assertEquals(3.0, optimum.getPoint()[2], 1.0e-10);

}
 

import org.apache.commons.math3.optimization.PointValuePair; //导入依赖的package包/类
@Test
public void testMoreEstimatedParametersSimple() {
    LinearProblem problem = new LinearProblem(new double[][] {
            { 3.0, 2.0,  0.0, 0.0 },
            { 0.0, 1.0, -1.0, 1.0 },
            { 2.0, 0.0,  1.0, 0.0 }
    }, new double[] { 7.0, 3.0, 5.0 });

    NonLinearConjugateGradientOptimizer optimizer =
        new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                new SimpleValueChecker(1e-6, 1e-6));
    PointValuePair optimum =
        optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 7, 6, 5, 4 });
    Assert.assertEquals(0, optimum.getValue(), 1.0e-10);

}