Package | Description |
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examples.jmdp | |
jmarkov.basic.exceptions |
This package contains the definition od the Exceptions thrown by jMarkov
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jmarkov.jmdp |
jMDP is used to solve Markov Decision Processes.
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jmarkov.jmdp.solvers |
This package contins the framwork of solvers used by jMDP to solve Markov Decision Processes.
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Modifier and Type | Method and Description |
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double |
OrderProcessing.averageOrders() |
double |
WagnerWhitin.getOptimalCost(int inventory)
Returns the optimal cost for this level of starting inventory.
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double |
CowHerd.getValue(int initQ) |
double |
CattleGrowth.getValue(int initWeight) |
double |
ControlProdNonEvents.getValueFunction(int invLevel)
Gets the optimal value function for this initial inventory level.
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double |
Bank2Queues.length() |
static void |
OrderProcessing.main(java.lang.String[] args) |
static void |
InfStochasticDemand.main(java.lang.String[] a)
Simple test Program.
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static void |
CTInventoryEvents.main(java.lang.String[] args)
This method just tests the class.
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static void |
CTInventory.main(java.lang.String[] args)
This method just tests the class.
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static void |
CowHerd.main(java.lang.String[] a)
Small test program
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static void |
ControlProduccion.main(java.lang.String[] a)
Test Program
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static void |
ControlProdNonEvents.main(java.lang.String[] a)
Small test program
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static void |
CattleGrowth.main(java.lang.String[] a)
Small test program
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static void |
Bank2Queues.main(java.lang.String[] args)
This method just tests the class.
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static void |
AccessControlFiniteHor.main(java.lang.String[] args)
This method just tests the class.
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static void |
AccessControl.main(java.lang.String[] args)
This method just tests the class.
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Modifier and Type | Class and Description |
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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.
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class |
StructureException
This exception is produced in shortest path problems if the conditions for
convergence are not met.
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Modifier and Type | Method and Description |
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Policy<S,A> |
MDP.getOptimalPolicy()
Returns the optimal policy.
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ValueFunction<S> |
MDP.getOptimalValueFunction()
Returns the optimal ValueFunction.
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ValueFunction<S> |
DTMDP.getSteadyStateProbabilities() |
ValueFunction<S> |
CTMDP.getSteadyStateProbabilities() |
void |
MDP.solve()
Solves the problem.
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Solution<S,A> |
DTMDP.solve(double interestRate)
Solves the problem with the given interest rate
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Solution<S,A> |
CTMDP.solve(double interestRate)
Solves the problem with the given interest rate
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Modifier and Type | Method and Description |
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java.lang.String |
FiniteSolver.bestPolicy(S initial)
Prints out the policy
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Solution<S,A> |
MPSXpressDiscounted.buildSolution()
A solution provided for a MPS File by Xpress-Optmizer has the
next framework.
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Solution<S,A> |
MPSXpressAverage.buildSolution() |
Solution<S,A> |
MPSQsOptDiscountedSolver.buildSolution() |
Solution<S,A> |
MPSQsOptAverageSolver.buildSolution() |
abstract Solution<S,A> |
MpsLpDiscountedSolver.buildSolution()
The implementator classes should override this class to build
the solution after the model has been solved.
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Solution<S,A> |
LPSolver.buildSolution()
The implementator classes should override this class to build
the solution after the model has been solved.
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Solution<S,A> |
LPBCLDiscountedSolver.buildSolution() |
Policy<S,A> |
Solver.getOptimalPolicy()
Gets the optimal policy.
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ValueFunction<S> |
Solver.getOptimalValueFunction()
Gets the optimal ValueFunction.
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void |
MPSXpressDiscounted.openFile() |
abstract Solution<S,A> |
Solver.solve()
Called to solve the problem.
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Solution<S,A> |
PolicyIterationSolverAvg.solve() |
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).
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void |
MPSXpressDiscounted.solveLP()
This is where the actual solving takes place.
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void |
MPSXpressAverage.solveLP() |
void |
MPSQsOptDiscountedSolver.solveLP() |
void |
MPSQsOptAverageSolver.solveLP() |
abstract void |
MpsLpDiscountedSolver.solveLP()
The implementator classes should override this class to solve
the problem using the mpsFile that has been created.
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void |
LPSolver.solveLP()
The implementator classes should override this class to solve
the problem using the mpsFile that has been created.
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void |
LPBCLDiscountedSolver.solveLP() |
Constructor and Description |
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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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