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.optaplanner.core.api.solver.Solver.solve()

Examples of org.optaplanner.core.impl.phase.Phase.solve()

Examples of org.xilaew.jampl.JAMPL.solve()

Examples of statechum.analysis.learning.rpnicore.LSolver.solve()

Examples of sudoku.Sudoku.SudokuService.solve()

Examples of sudoku.Sudoku.SudokuService.BlockingInterface.solve()

Examples of uk.ac.cranfield.thesis.client.service.RungeKuttaSolverServiceAsync.solve()