List of solve() Examples

Examples of solve()

Jama.CholeskyDecomposition.solve()
Solve A*X = B @param B A Matrix with as many rows as A and any number of columns. @return X so that L*L'*X = B @exception IllegalArgumentException Matrix row dimensions must agree. @exception RuntimeException Matrix is not symmetric positive definite.
Jama.LUDecomposition.solve()
Solve A*X = B @param B A Matrix with as many rows as A and any number of columns. @return X so that L*U*X = B(piv,:) @exception IllegalArgumentException Matrix row dimensions must agree. @exception RuntimeException Matrix is singular.
Jama.QRDecomposition.solve()
Least squares solution of A*X = B @param B A Matrix with as many rows as A and any number of columns. @return X that minimizes the two norm of Q*R*X-B. @exception IllegalArgumentException Matrix row dimensions must agree. @exception RuntimeException Matrix is rank deficient.
aima.core.search.csp.SolutionStrategy.solve()
Returns a solution to the specified CSP, which specifies values for all the variables such that the constraints are satisfied. @param csp a CSP to solve @return a solution to the specified CSP, which specifies values for allthe variables such that the constraints are satisfied.
cern.colt.matrix.linalg.Algebra.solve()
Solves A*X = B. @return X; a new independent matrix; solution if A is square, least squares solution otherwise.
cern.colt.matrix.linalg.LUDecompositionQuick.solve()
Solves the system of equations A*X = B (in-place). Upon return B is overridden with the result X, such that L*U*X = B(piv). @param B A vector with B.size() == A.rows(). @exception IllegalArgumentException if B.size() != A.rows(). @exception IllegalArgumentException if A is singular, that is, if !isNonsingular(). @exception IllegalArgumentException if A.rows() < A.columns().
choco.cp.solver.CPSolver.solve()
choco.kernel.solver.Solver.solve()
com.chenshuo.muduo.protorpc.sudoku.SudokuProto.SudokuService.solve()
com.mockturtlesolutions.snifflib.invprobs.NMSimplex.solve()
com.nr.interp.Laplace_interp.solve()
com.nr.la.Bandec.solve()
Given a right-hand side vector b[0..n-1], solves the band-diagonal linear equations A*x = b. The solution vector x is returned as x[0..n-1]. @param b @param x
com.nr.la.Cholesky.solve()
Solve the set of n linear equations A*x = b, where a is a positive-definite symmetric matrix whose Cholesky decomposition has been stored. b[0..n-1] is input as the right-hand side vector. The solution vector is returned in x[0..n-1]. @param b @param x
com.nr.la.LUdcmp.solve()
Solves the set of n linear equations A*x = b using the stored LU decomposition of A. b[0..n-1] is input as the right-hand side vector b, while x returns the solution vector x; b and x may reference the same vector, in which case the solution overwrites the input. This routine takes into account the possibility that b will begin with many zero elements, so it is efficient for use in matrix inversion. @param b @param x
com.nr.la.QRdcmp.solve()
Solve the set of n linear equations A*x = b. b[0..n-1] is input as the right-hand side vector, and x[0..n-1] is returned as the solution vector @param b @param x
com.nr.la.SVD.solve()
com.opengamma.analytics.financial.model.finitedifference.ConvectionDiffusionPDESolver.solve()
com.opengamma.analytics.financial.model.finitedifference.CoupledFiniteDifference.solve()
com.opengamma.analytics.financial.model.finitedifference.ExtendedCoupledFiniteDifference.solve()
com.opengamma.analytics.financial.model.finitedifference.ThetaMethodFiniteDifference.solve()
com.opengamma.analytics.financial.model.volatility.smile.fitting.MixedLogNormalModelFitter.solve()
com.opengamma.analytics.financial.model.volatility.smile.fitting.SABRModelFitter.solve()
com.opengamma.analytics.math.interpolation.PSplineFitter.solve()
Fits a curve to x-y data @param x The independent variables @param y The dependent variables @param sigma The error on the y variables @param xa The lowest value of x @param xb The highest value of x @param nKnots Number of knots (note, the actual number of basis splines and thus fitted weights, equals nKnots + degree) @param degree The degree of the basis function - 0 is piecewise constant, 1 is a sawtooth function (i.e. two straight lines joined in the middle), 2 gives three quadratic sections joined together, etc. For a large value of degree, the basis function tends to a gaussian @param lambda The weight given to the penalty function @param differenceOrder applies the penalty the the nth order difference in the weights, so a differenceOrder of 2 will penalise large 2nd derivatives etc @return The results of the fit
com.opengamma.analytics.math.linearalgebra.DecompositionResult.solve()
Solves $\mathbf{A}x = b$ where $\mathbf{A}$ is a (decomposed) matrix and $b$ is a vector. @param b a vector, not null @return the vector x
com.opengamma.analytics.math.linearalgebra.LUDecompositionResult.solve()
com.opengamma.analytics.math.linearalgebra.SVDecompositionResult.solve()
com.opengamma.analytics.math.statistics.leastsquare.NonLinearLeastSquare.solve()
Use this when the model is in the ParameterizedFunction form and analytic parameter sensitivity is not available @param x Set of measurement points @param y Set of measurement values @param func The model in ParameterizedFunction form (i.e. takes measurement points and a set of parameters and returns a model value) @param startPos Initial value of the parameters @return A LeastSquareResults object
csp.backends.MinionSolver.solve()
csp.backends.TailorSolver.solve()
edu.cmu.relativelayout.matrix.RelativeMatrix.solve()
Solves this matrix and returns a map containing keys for every variable that has been added to the matrix whose values are the solutions for those variables.
gannuOP.algorithms.Optimizer.solve()
Looks for the global minimum value a function. @param parameters The parameters used by the algorithm in the format "parameter1:value1;...parameterN:valueN". @param fx The optimized function. @param maxFes The maximum number of function evaluations that the algorithm is allowed to perform. @param reportFes A report will be created every reportFes function evaluations. Use maxFes value for generating just one report. @param precision Target precision for error value. @return A list with the reports of the progresses of the algorithm. @throws Exception
gannuWSD.algorithms.FirstSense.solve()
gannuWSD.algorithms.WSDAlgorithm.solve()
Tells the disambiguation algorithm to solve an input document. @param document The disambiguated document. @param backoff Back-off strategy. @param tie Algorithm to be used for solving ties. @param dict Base dictionary. @return Decisions made by this algorithm.
hu.u_szeged.nbo.client.model.ResourceAllocation.solve()
hu.u_szeged.nbo.geometria.main.algorithm.GeomAlgorithm.solve()
The main solver function of GeomAlgorithm. @param resources @return @throws GeomKAPMatrixFillException
hu.u_szeged.nbo.res_alloc.algorithm.GreedyAlgorithm.solve()
hu.u_szeged.nbo.res_alloc.main.Solver.solve()
hu.u_szeged.nbo.server.jni.JLPClass.solve()
hu.u_szeged.nbo.server.jni.JMILPClass.solve()
jinngine.physics.solver.ConjugateGradients.solve()
jinngine.physics.solver.Solver.solve()
Given a list of constraints, solve the corresponding NCP @param constraints List of constraints @param epsilon TODO @return error
jmathexpr.arithmetic.equation.Equation.solve()
Solves this equation step by step. The steps are stored internally. @return a set of zero or one or infinite number of elements: the solutions of this equation @throws EquationSolveException if the equation cannot be solved
jmt.analytical.CommandLineSolver.solve()
Solves the model contained in the file. The results are saved into the file itself. @param file the XML file containing the model @return true if the model has been correctly solved
jmt.analytical.SolverDispatcher.solve()
Solves the model in file. @throws jmt.common.exception.SolverException if there were errors operation was not successful
jmt.analytical.SolverMulti.solve()
Must be implemented to create a multi class model solver.
jmt.analytical.SolverMultiClosedAQL.solve()
jmt.analytical.SolverMultiClosedBardSchweitzer.solve()
jmt.analytical.SolverMultiClosedChow.solve()
jmt.analytical.SolverMultiClosedLinearizer.solve()
jmt.analytical.SolverMultiClosedMVA.solve()
Solves the model, using an appropriate technique (LI or LD model).
"input(...)" method must have been called before solving the model!!
jmt.analytical.SolverMultiMixed.solve()
jmt.analytical.SolverMultiOpen.solve()
Solves the model, using an appropriate technique (LI or LD model).
jmt.analytical.SolverSingleClosedMVA.solve()
Solves a single class closed model using MVA algorithm.
"input(...)" method must have been called before solving the model!!

Reference:
G.Balbo, S.C.Bruell, L.Cerchio, D.Chiaberto, L.Molinatti
"Mean Value Analysis of Closed Load Dependent Networks"
kodkod.engine.satlab.SATMinSolver.solve()
kodkod.engine.satlab.SATSolver.solve()
Returns true if there is a satisfying assignment for this.clauses. Otherwise returns false. If this.clauses are satisfiable, the satisfying assignment for a given variable can be obtained by calling {@link #valueOf(int)}. If the satisfiability of this.clauses cannot be determined within the given number of seconds, a TimeoutException is thrown. @return true if this.clauses are satisfiable; otherwise false. @throws SATAbortedException -- the call to solve was cancelled orcould not terminate normally.
mikera.matrixx.solve.impl.CholeskyLDUSolver.solve()

Using the decomposition, finds the value of 'X' in the linear equation below:
A*x = b
where A has dimension of n by n, x and b are n by m dimension.

*Note* that 'b' and 'x' can be the same matrix instance.
@param B A matrix that is n by m. Not modified. @param X An n by m matrix where the solution is writen to. Modified.
mikera.matrixx.solve.impl.lu.LUSolver.solve()
mikera.matrixx.solve.impl.qr.QRHouseColSolver.solve()
Solves for X using the QR decomposition. @param B A matrix that is n by m. Not modified.
minesweeper.ai.players.AIPlayer.solve()
minesweeper.ai.players.ProbablisticSearchTreeAI.solve()
net.sf.javailp.Solver.solve()
no.uib.cipr.matrix.BandCholesky.solve()
Computes A\B, overwriting B
no.uib.cipr.matrix.BandLU.solve()
Computes A\B, overwriting B
no.uib.cipr.matrix.DenseCholesky.solve()
Solves for B, overwriting it on return
no.uib.cipr.matrix.DenseLU.solve()
Computes A\B, overwriting B
no.uib.cipr.matrix.PackCholesky.solve()
Solves for B, overwriting it on return
no.uib.cipr.matrix.sparse.IterativeSolver.solve()
Solves the given problem, writing result into the vector. @param A Matrix of the problem @param b Right hand side @param x Solution is stored here. Also used as initial guess @return The solution vector x
org.apache.commons.math.analysis.BrentSolver.solve()
Find a zero in the given interval.
Throws ConvergenceException if the values of the function at the endpoints of the interval have the same sign. @param min the lower bound for the interval. @param max the upper bound for the interval. @param initial the start value to use (ignored). @return the value where the function is zero @throws ConvergenceException the maximum iteration count is exceeded @throws FunctionEvaluationException if an error occurs evaluatingthe function @throws IllegalArgumentException if initial is not between min and max
org.apache.commons.math.analysis.UnivariateRealSolver.solve()
Solve for a zero root in the given interval. A solver may require that the interval brackets a single zero root. @param min the lower bound for the interval. @param max the upper bound for the interval. @return a value where the function is zero @throws ConvergenceException if the maximum iteration count is exceededor the solver detects convergence problems otherwise. @throws FunctionEvaluationException if an error occurs evaluating thefunction @throws IllegalArgumentException if min > max or the endpoints do notsatisfy the requirements specified by the solver
org.apache.commons.math.analysis.solvers.BrentSolver.solve()
{@inheritDoc}
org.apache.commons.math.linear.DecompositionSolver.solve()
Solve the linear equation A × X = B for matrices A.
The A matrix is implicit, it is provided by the underlying decomposition algorithm.
@param b right-hand side of the equation A × X = B @return a vector X that minimizes the two norm of A × X - B @exception IllegalArgumentException if matrices dimensions don't match @exception InvalidMatrixException if decomposed matrix is singular
org.apache.commons.math.linear.RealMatrixImpl.solve()
Returns a matrix of (column) solution vectors for linear systems with coefficient matrix = this and constant vectors = columns of b. @param b array of constant forming RHS of linear systems toto solve @return solution array @throws IllegalArgumentException if this.rowDimension != row dimension @throws InvalidMatrixException if this matrix is not square or is singular
org.apache.mahout.math.QRDecomposition.solve()
Least squares solution of A*X = B; returns X. @param B A matrix with as many rows as A and any number of columns. @return X that minimizes the two norm of Q*R*X - B. @throws IllegalArgumentException if B.rows() != A.rows().
org.apache.mahout.math.als.AlternateLeastSquaresSolver.solve()
org.apache.mahout.math.als.AlternatingLeastSquaresSolver.solve()
org.apache.mahout.math.als.ImplicitFeedbackAlternatingLeastSquaresSolver.solve()
org.apache.mahout.math.hadoop.stochasticsvd.qr.GivensThinSolver.solve()
org.drools.planner.core.Solver.solve()
Solves the planning problem. It can take minutes, even hours or days before this method returns, depending on the termination configuration. To terminate a {@link Solver} early, call {@link #terminateEarly()}. @see #terminateEarly()
org.drools.planner.core.phase.SolverPhase.solve()
org.drools.solver.core.Solver.solve()
org.ejml.alg.block.linsol.qr.BlockQrHouseHolderSolver.solve()
org.ejml.alg.dense.linsol.AdjustableLinearSolver.solve()
org.ejml.alg.dense.linsol.lu.LinearSolverLu.solve()
org.ejml.factory.LinearSolver.solve()

Solves for X in the linear system, A*X=B.

In some implementations 'B' and 'X' can be the same instance of a variable. Call {@link #modifiesB()} to determine if 'B' is modified.
@param B A matrix ℜ ^{m × p}. Might be modified. @param X A matrix ℜ ^{n × p}, where the solution is written to. Modified.
org.encog.mathutil.matrices.decomposition.CholeskyDecomposition.solve()
Solve A*X = B. @param b A Matrix with as many rows as A and any number of columns. @return X so that L*L'*X = b.
org.encog.mathutil.matrices.decomposition.LUDecomposition.Solve()
Solve A*X = B @param B A Matrix with as many rows as A and any number of columns. @return X so that L*U*X = B(piv,:) @exception IllegalArgumentException Matrix row dimensions must agree. @exception RuntimeException Matrix is singular.
org.encog.mathutil.matrices.decomposition.QRDecomposition.solve()
Least squares solution of A*X = B @param B A Matrix with as many rows as A and any number of columns. @return X that minimizes the two norm of Q*R*X-B. @exception IllegalArgumentException Matrix row dimensions must agree. @exception RuntimeException Matrix is rank deficient.
org.integratedmodelling.riskwiz.jtree.JTSolver.Solve()
org.integratedmodelling.riskwiz.jtree.JTSolverPN.Solve()
org.jquantlib.math.solvers1D.Bisection.solve()
org.jquantlib.math.solvers1D.Brent.solve()
org.jquantlib.math.solvers1D.FalsePosition.solve()
org.jquantlib.math.solvers1D.Newton.solve()
org.jquantlib.math.solvers1D.NewtonSafe.solve()
org.jquantlib.math.solvers1D.Ridder.solve()
org.jquantlib.math.solvers1D.Secant.solve()
org.moxie.Solver.solve()
org.netbeans.modules.php.nette.editor.resolvers.HtmlPhpResolver.solve()
org.netbeans.modules.php.nette.editor.resolvers.LatteResolver.solve()
org.openntf.formula.ASTNode.solve()
Use this method to solve a formula
org.optaplanner.core.api.solver.Solver.solve()
Solves the planning problem. It can take minutes, even hours or days before this method returns, depending on the termination configuration. To terminate a {@link Solver} early, call {@link #terminateEarly()}. @param planningProblem never null, usually its planning variables are uninitialized @see #terminateEarly()
org.optaplanner.core.impl.phase.Phase.solve()
org.xilaew.jampl.JAMPL.solve()
Ask AMPL to solve the current Problem. This method blocks until a solve result is returned. @return @throws IOException
statechum.analysis.learning.rpnicore.LSolver.solve()
Solves the system of equations using the external solver if it is available, otherwise falls back on Colt. @param threads the number of threads to useThrows IllegalArgumentException of the matrix is singular.
sudoku.Sudoku.SudokuService.solve()
sudoku.Sudoku.SudokuService.BlockingInterface.solve()
uk.ac.cranfield.thesis.client.service.RungeKuttaSolverServiceAsync.solve()

Examples of org.apache.commons.math.linear.DecompositionSolver.solve()

    direction.normalize();
    RealMatrix coefficients = new Array2DRowRealMatrix(new double[][] { { direction.getX(), -(sideVectX) },
        { direction.getY(), -(sideVectY) } }, false);
    DecompositionSolver solver = new LUDecompositionImpl(coefficients).getSolver();
    RealVector constants = new ArrayRealVector(new double[] { sideX - roboter1.getX(), sideY - roboter1.getY() }, false);
    RealVector solution = solver.solve(constants);


    double r = solution.getEntry(0);
    // double s = solution.getEntry(1);


    double res1 = roboter1.getX() + direction.getX() * r;

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Examples of org.apache.commons.math.linear.RealMatrixImpl.solve()

            }


            try {


                // solve the linearized least squares problem
                RealMatrix dX = a.solve(b);


                // update the estimated parameters
                for (int i = 0; i < parameters.length; ++i) {
                    parameters[i].setEstimate(parameters[i].getEstimate() + dX.getEntry(i, 0));
                }

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Examples of org.apache.mahout.math.QRDecomposition.solve()

    Matrix b = m.viewPart(0, rows, 0, 1);


    Matrix ata = a.transpose().times(a);
    Matrix atb = a.transpose().times(b);
    QRDecomposition s = new QRDecomposition(ata);
    Matrix r = s.solve(atb).transpose();
    assertEquals(expectedCoefficient, r.get(0, 0), 0.2);
    return r.times(new DenseVector(new double[]{Math.log(currentIndex), 1})).get(0);
  }


  private static int hapaxCount(ChineseRestaurant s) {

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Examples of org.apache.mahout.math.als.AlternateLeastSquaresSolver.solve()

              List<Vector> featureVectors = new ArrayList<Vector>();
              while (itemIDsFromUser.hasNext()) {
                long itemID = itemIDsFromUser.nextLong();
                featureVectors.add(features.getItemFeatureColumn(itemIndex(itemID)));
              }
              Vector userFeatures = solver.solve(featureVectors, ratingVector(userPrefs), lambda, numFeatures);
              features.setFeatureColumnInU(userIndex(userID), userFeatures);
            }
          });
        }
      } finally {

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Examples of org.apache.mahout.math.als.AlternatingLeastSquaresSolver.solve()

              List<Vector> featureVectors = Lists.newArrayList();
              while (itemIDsFromUser.hasNext()) {
                long itemID = itemIDsFromUser.nextLong();
                featureVectors.add(features.getItemFeatureColumn(itemIndex(itemID)));
              }
              Vector userFeatures = solver.solve(featureVectors, ratingVector(userPrefs), lambda, numFeatures);
              features.setFeatureColumnInU(userIndex(userID), userFeatures);
            }
          });
        }
      } finally {

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Examples of org.apache.mahout.math.als.ImplicitFeedbackAlternatingLeastSquaresSolver.solve()

                long itemID = itemIDsFromUser.nextLong();
                featureVectors.add(features.getItemFeatureColumn(itemIndex(itemID)));
              }


              Vector userFeatures = usesImplicitFeedback
                  ? implicitFeedbackSolver.solve(sparseUserRatingVector(userPrefs))
                  : AlternatingLeastSquaresSolver.solve(featureVectors, ratingVector(userPrefs), lambda, numFeatures);


              features.setFeatureColumnInU(userIndex(userID), userFeatures);
            }
          });

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Examples of org.apache.mahout.math.hadoop.stochasticsvd.qr.GivensThinSolver.solve()

    m.setQuick(2, 0, 7);
    m.setQuick(2, 1, 8);
    m.setQuick(2, 2, 9);


    GivensThinSolver qrSolver = new GivensThinSolver(m.rowSize(), m.columnSize());
    qrSolver.solve(m);


    Matrix qtm = new DenseMatrix(qrSolver.getThinQtTilde());


    assertOrthonormality(qtm.transpose(), false, SVD_EPSILON);

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Examples of org.drools.planner.core.Solver.solve()

                        statistic = solverStatisticType.create();
                        unsolvedSolutionFileToStatisticMap.put(unsolvedSolutionFile, statistic);
                    }
                    statistic.addListener(solver, solverBenchmark.getName());
                }
                solver.solve();
                result.setTimeMillisSpend(solver.getTimeMillisSpend());
                Solution solvedSolution = solver.getBestSolution();
                result.setScore(solvedSolution.getScore());
                if (solverStatisticType != SolverStatisticType.NONE) {
                    SolverStatistic statistic = unsolvedSolutionFileToStatisticMap.get(unsolvedSolutionFile);

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Examples of org.drools.planner.core.phase.SolverPhase.solve()


    protected void runSolverPhases() {
        Iterator<SolverPhase> it = solverPhaseList.iterator();
        while (!termination.isSolverTerminated(solverScope) && it.hasNext()) {
            SolverPhase solverPhase = it.next();
            solverPhase.solve(solverScope);
            if (it.hasNext()) {
                solverScope.setWorkingSolutionFromBestSolution();
            }
        }
        // TODO support doing round-robin of phases (only non-construction heuristics)

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Examples of org.drools.solver.core.Solver.solve()

        configurer.configure(SOLVER_CONFIG);
        Solver solver = configurer.buildSolver();
        SolutionDao solutionDao = new XstreamSolutionDaoImpl();
        Solution startingSolution = solutionDao.readSolution(getClass().getResourceAsStream(UNSOLVED_DATA));
        solver.setStartingSolution(startingSolution);
        solver.solve();
        Solution bestSolution = solver.getBestSolution();
        assertNotNull(bestSolution);
        double bestScore = solver.getBestScore();
        assertEquals(0.0, bestScore);
    }

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