List of minimize() Examples

Examples of minimize()

com.granule.CSSFastMin.minimize()
com.granule.JSFastWhitespaceRemover.minimize()
com.nr.min.Amoeba.minimize()
com.nr.min.Brent.minimize()
com.nr.min.Dbrent.minimize()
com.nr.min.Frprmn.minimize()
com.nr.min.Golden.minimize()
com.nr.min.Powell.minimize()
com.opengamma.analytics.math.minimization.ConjugateDirectionVectorMinimizer.minimize()
{@inheritDoc}
com.opengamma.analytics.math.minimization.ConjugateGradientVectorMinimizer.minimize()
{@inheritDoc}
dk.brics.automaton.Automaton.minimize()
See {@link MinimizationOperations#minimize(Automaton)}.
edu.stanford.nlp.optimization.GoldenSectionLineSearch.minimize()
edu.stanford.nlp.optimization.LineSearcher.minimize()
Attempts to find an unconstrained minimum of the objective function starting at initial, within functionTolerance. @param function the objective function
edu.stanford.nlp.optimization.QNMinimizer.minimize()
kodkod.engine.Proof.minimize()
Minimizes the proof of this.log.formula's unsatisfiability using the specified proof reduction strategy. The strategy argument is ignored (it can be null) if this.formula is trivially unsatisfiable with respect to this.bounds. In that case, the core is reduced using the trivial strategy that does one of the following: (1) if there is a root that simplified to FALSE, sets the minimal core to that root; or (2) if not, then there must be two roots that translated to x and -x, where x is a boolean literal, so we pick those two as the minimal core.
Note: the core minimization is performed at the level of the transformed formula, not the original formula if the problem was translated with a non-zero {@linkplain Options#coreGranularity() core granularity} setting.In other words, after this method has been called, {@linkplain #highLevelCore() highLevelCore()} returns a map M such that M.keySet() is a minimal core with respect to this.log.bounds. In contrast, {@linkplain Nodes#allRoots(Formula,java.util.Collection) Nodes.roots(this.log.originalFormula, M.values())} is unsatisfiable with respect this.log.originalBounds. These formulas, however, do not necessarily form a minimal core of this.log.originalFormula if the original problem was translated with a non-zero {@linkplain Options#coreGranularity() core granularity} setting.
@effects minimizes the proof of this.log.formula's unsatisfiabilityusing the specified proof reduction strategy (or the trivial strategy if this.formula is trivially unsatisfiable with respect to this.bounds). @see kodkod.engine.satlab.ReductionStrategy
org.apache.commons.math.optimization.NelderMead.minimize()
org.apache.tapestry5.services.assets.ResourceMinimizer.minimize()
Checks the {@linkplain StreamableResource#getContentType() content type} of the resource and applies anappropriate minimization to it if possible. @param resource to minimize @return the same resource, or a minimized replacement for the resource @throws IOException
org.jquantlib.math.optimization.LevenbergMarquardt.minimize()
org.rstudio.core.client.theme.res.ThemeStyles.minimize()

Examples of com.nr.min.Golden.minimize()

    Golden gold = new Golden();
    for (i=0;i<N;i++) {
      a=i;
      b=a+span;
      gold.bracket(a,b,this);
      min=gold.minimize(this);   // Minimum of bessj0
      localflag = localflag || (abs(Bessj1_Golden(min)) > sbeps);
    }


    globalflag = globalflag || localflag;
    if (localflag) {

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Examples of com.nr.min.Powell.minimize()

    // Test Powell
    System.out.println("Testing Powell, interface1");


    Powell pow1 = new Powell(this);
    p[0]=p[1]=p[2]=0.0;
    p=pow1.minimize(p);
    f0=pow1.fret;
    d0=sqrt(SQR(p[0])+SQR(p[1])+SQR(p[2]));


    for (i=0;i<N;i++) {
      theta=pi*i/N;

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Examples of com.nr.min.Powell.minimize()

      theta=pi*i/N;
      phi=pi*i/N;
      p[0]=sin(phi)*cos(theta);
      p[1]=sin(phi)*sin(theta);
      p[2]=cos(phi);
      p=pow1.minimize(p);
      f=pow1.fret;
      d=sqrt(SQR(p[0])+SQR(p[1])+SQR(p[2]));
//      System.out.printf(abs(d-d0) << " " << abs(f-f0));


      localflag = localflag || abs(d-d0) > sbeps1;

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Examples of com.nr.min.Powell.minimize()

      ximat[1][1]=cos(theta)*sin(phi);
      ximat[2][1]=-sin(theta);
      ximat[0][2]=-sin(theta);
      ximat[1][2]=cos(theta);
      ximat[2][2]=0.0;
      p=pow2.minimize(p,ximat);
      f=pow2.fret;
      d=sqrt(SQR(p[0])+SQR(p[1])+SQR(p[2]));
//      System.out.printf(abs(d-d0) << " " << abs(f-f0));


      localflag = localflag || abs(d-d0) > sbeps1;

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Examples of com.opengamma.analytics.math.minimization.ConjugateDirectionVectorMinimizer.minimize()

      }
    };
    final ScalarMinimizer lineMinimizer = new BrentMinimizer1D();
    final ConjugateDirectionVectorMinimizer minimzer = new ConjugateDirectionVectorMinimizer(lineMinimizer, 1e-6, 10000);
    final DoubleMatrix1D fp = transforms.transform(new DoubleMatrix1D(initialFitParameters));
    final DoubleMatrix1D minPos = minimzer.minimize(function, fp);
    final double chiSquare = function.evaluate(minPos);
    final DoubleMatrix1D res = transforms.inverseTransform(minPos);
    return new LeastSquareResultsWithTransform(new LeastSquareResults(chiSquare, res, new DoubleMatrix2D(new double[N_PARAMETERS][N_PARAMETERS])), transforms);
    // return new LeastSquareResults(chiSquare, res, new DoubleMatrix2D(new double[N_PARAMETERS][N_PARAMETERS]));
  }

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Examples of com.opengamma.analytics.math.minimization.ConjugateGradientVectorMinimizer.minimize()


  public void solveExactFromChiSqTest() {
    final DoubleMatrix1D start = new DoubleMatrix1D(new double[] {1.2, 0.8, -0.2, -0.3 });
    final Function1D<DoubleMatrix1D, Double> f = getChiSqFunction(X, Y, SIGMA, PARAM_FUNCTION);
    final ConjugateGradientVectorMinimizer minimizer = new ConjugateGradientVectorMinimizer(new BrentMinimizer1D());
    final DoubleMatrix1D solution = minimizer.minimize(f, start);
    assertEquals(0.0, f.evaluate(solution), 1e-8);
    assertEquals(1.0, solution.getEntry(0), 1e-8);
    assertEquals(1.0, solution.getEntry(1), 1e-8);
    assertEquals(0.0, solution.getEntry(2), 1e-8);
    assertEquals(0.0, solution.getEntry(3), 1e-8);

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Examples of com.opengamma.analytics.math.minimization.ConjugateGradientVectorMinimizer.minimize()

        final double[] start = new double[nodePos.size()];
        for (int i = 0; i < start.length; i++) {
          start[i] = VALUE;
        }


    final DoubleMatrix1D res = minimizer.minimize(fom, new DoubleMatrix1D(start));
    final double chiSquare = fom.evaluate(res);
     System.out.println(res);
     System.out.println("chi2: " + chiSquare);


     final List<Pair<double[], Double>> weights = combineWeightsAndPos(nodePos, res);

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Examples of dk.brics.automaton.Automaton.minimize()

                }
            }
        }
        b.setDeterministic(false);
        b.reduce();
        b.minimize();
        return b;
    }
    
    @Override
    public String toString() {

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Examples of dk.brics.automaton.Automaton.minimize()

        Set<StatePair> epsilons = new HashSet<StatePair>();
        for (State s : b.getStates()) {
            epsilons.add(new StatePair(s, accept));
        }
        b.addEpsilons(epsilons);
        b.minimize();
        return b;
    }


    @Override
    public String toString() {

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Examples of dk.brics.automaton.Automaton.minimize()

                }
            }
        }
        b.setDeterministic(false);
        b.reduce();
        b.minimize();
        return b;
    }


    @Override
    public String toString() {

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