-
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()