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Packages that use SolverException | |
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jmarkov.basic.exceptions | This package contains the definition od the Exceptions thrown by jMarkov. |
jmarkov.jmdp | jMDP is used to solve Markov Decision Processes. |
jmarkov.jmdp.solvers | This package contins the framwork of solvers used by jMDP to solve Markov Decision Processes. |
Uses of SolverException in jmarkov.basic.exceptions |
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Subclasses of SolverException in jmarkov.basic.exceptions | |
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class |
NotUnichainException
This Exception should be thrown by the SteadyStateSolver if it detects that there is not a unique solution to the stationary probabilities. |
class |
StructureException
This exception is produced in shortest path problems if the conditions for convergence are not met. |
Uses of SolverException in jmarkov.jmdp |
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Methods in jmarkov.jmdp that throw SolverException | |
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Policy<S,A> |
MDP.getOptimalPolicy()
Returns the optimal policy. |
ValueFunction<S> |
MDP.getOptimalValueFunction()
Returns the optimal ValueFunction. |
ValueFunction<S> |
DTMDP.getSteadyStateProbabilities()
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ValueFunction<S> |
CTMDP.getSteadyStateProbabilities()
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void |
MDP.solve()
Solves the problem. |
Solution<S,A> |
DTMDP.solve(double interestRate)
Solves the problem with the given interest rate |
Solution<S,A> |
CTMDP.solve(double interestRate)
Solves the problem with the given interest rate |
Uses of SolverException in jmarkov.jmdp.solvers |
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Methods in jmarkov.jmdp.solvers that throw SolverException | |
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java.lang.String |
FiniteSolver.bestPolicy(S initial)
Prints out the policy |
abstract Solution<S,A> |
MpsLpDiscountedSolver.buildSolution()
The implementator classes should override this class to build the solution after the model has been solved. |
Solution<S,A> |
LPSolver.buildSolution()
The implementator classes should override this class to build the solution after the model has been solved. |
Solution<S,A> |
LPBCLDiscountedSolver.buildSolution()
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Policy<S,A> |
Solver.getOptimalPolicy()
Gets the optimal policy. |
ValueFunction<S> |
Solver.getOptimalValueFunction()
Gets the optimal ValueFunction. |
abstract Solution<S,A> |
Solver.solve()
Called to solve the problem. |
Solution<S,A> |
PolicyIterationSolver.solve()
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Solution<S,A> |
MpsLpDiscountedSolver.solve()
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Solution<S,A> |
MpsLpAverageSolver.solve()
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Solution<S,A> |
LPBCLDiscountedSolver.solve()
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Solution<S,A> |
LPBCLAverageSolver.solve()
Linear Programming Average Solver is a tool that builds the solution based on the MDP's mathematical background given by Puterman and the software provided by XpressMP (BCL libraries). |
abstract void |
MpsLpDiscountedSolver.solveLP()
The implementator classes should override this class to solve the problem using the mpsFile that has been created. |
void |
LPSolver.solveLP()
The implementator classes should override this class to solve the problem using the mpsFile that has been created. |
void |
LPBCLDiscountedSolver.solveLP()
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protected ValueFunction<S> |
PolicyIterationSolver.solveMatrix()
This method is used by the PolicyIterationSolver to solve the linear system of equations to determine the value functions of each state for a given policy. |
Constructors in jmarkov.jmdp.solvers that throw SolverException | |
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ProbabilitySolver(CTMDP<S,A> problem)
Initializes a new solver for continuous chains and solves the probabilities for the optimal policy. |
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ProbabilitySolver(DTMDP<S,A> problem)
Initializes a new solver for discrete chains and solves the probabilities for the optimal policy. |
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