Uses of Class
jmarkov.basic.exceptions.SolverException

Packages that use SolverException
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
 

Subclasses of SolverException in jmarkov.basic.exceptions
 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
 

Methods in jmarkov.jmdp that throw SolverException
 Policy<S,A> MDP.getOptimalPolicy()
          Returns the optimal policy.
 ValueFunction<S> MDP.getOptimalValueFunction()
          Returns the optimal ValueFunction.
 ValueFunction<S> DTMDP.getSteadyStateProbabilities()
           
 ValueFunction<S> CTMDP.getSteadyStateProbabilities()
           
 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
 

Methods in jmarkov.jmdp.solvers that throw SolverException
 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()
           
 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()
           
 Solution<S,A> MpsLpDiscountedSolver.solve()
           
 Solution<S,A> MpsLpAverageSolver.solve()
           
 Solution<S,A> LPBCLDiscountedSolver.solve()
           
 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()
           
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
ProbabilitySolver(CTMDP<S,A> problem)
          Initializes a new solver for continuous chains and solves the probabilities for the optimal policy.
ProbabilitySolver(DTMDP<S,A> problem)
          Initializes a new solver for discrete chains and solves the probabilities for the optimal policy.