trait MPSolver extends StrictLogging
Solver interface that describes a mathematical programming solver. It should be extended by all the individual solvers to be supported in the library.
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- abstract type Solver
Abstract Value Members
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abstract
def
addConstraint(mpConstraint: MPConstraint): Unit
Add a mathematical programming constraint to the solver.
Add a mathematical programming constraint to the solver.
- mpConstraint
the mathematical programming constraint
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abstract
def
buildModel(numberOfVars: Int): Unit
Problem builder, should configure the solver and append mathematical model variables and constraints.
Problem builder, should configure the solver and append mathematical model variables and constraints.
- numberOfVars
number of variables in the model
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abstract
def
getVarValue(colId: Int): Double
Get value of the variable in the specified position.
Get value of the variable in the specified position. Solution should exist in order for a value to exist.
- colId
position of the variable
- returns
the value of the variable in the solution
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abstract
def
release(): Unit
Release the memory of the solver.
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abstract
def
setBinary(colId: Int): Unit
Set the column / variable as an binary integer variable
Set the column / variable as an binary integer variable
- colId
position of the variable
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abstract
def
setBounds(colId: Int, lower: Double, upper: Double): Unit
Set bounds of variable in the specified position.
Set bounds of variable in the specified position.
- colId
position of the variable
- lower
domain lower bound
- upper
domain upper bound
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abstract
def
setFloat(colId: Int): Unit
Set the column/variable as a float variable
Set the column/variable as a float variable
- colId
position of the variable
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abstract
def
setInteger(colId: Int): Unit
Set the column/variable as an integer variable
Set the column/variable as an integer variable
- colId
position of the variable
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abstract
def
setObjective(objective: Expression, minimize: Boolean): Unit
Add objective expression to be optimized by the solver.
Add objective expression to be optimized by the solver.
- objective
the expression to be optimized
- minimize
flag for minimization instead of maximization
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abstract
def
setTimeout(limit: Int): Unit
Set a time limit for solver optimization.
Set a time limit for solver optimization. After the limit is reached the solver stops running.
- limit
the time limit
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abstract
def
setUnboundLowerBound(colId: Int): Unit
Set lower bound to unbounded (infinite)
Set lower bound to unbounded (infinite)
- colId
position of the variable
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abstract
def
setUnboundUpperBound(colId: Int): Unit
Set upper bound to unbounded (infinite).
Set upper bound to unbounded (infinite).
- colId
position of the variable
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abstract
def
solve(preSolve: PreSolve = DISABLED): SolutionStatus
Solves the optimization problem.
Solves the optimization problem.
- preSolve
pre-solving mode
- returns
status code indicating the nature of the solution
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abstract
val
underlyingSolver: Solver
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final
def
!=(arg0: Any): Boolean
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def
##(): Int
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def
==(arg0: Any): Boolean
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val
_objectiveValue: Option[Double]
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val
_solution: Array[Double]
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val
_solutionStatus: SolutionStatus
- Attributes
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-
def
addAllConstraints(constraints: ArrayBuffer[MPConstraint]): Unit
Add all given mathematical programming constraints to the solver.
Add all given mathematical programming constraints to the solver.
- constraints
an array buffer containing the constraints
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final
def
asInstanceOf[T0]: T0
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clone(): AnyRef
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eq(arg0: AnyRef): Boolean
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equals(arg0: Any): Boolean
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getClass(): Class[_]
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val
logger: Logger
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ne(arg0: AnyRef): Boolean
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notify(): Unit
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notifyAll(): Unit
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val
numberOfCons: Int
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def
numberOfConstraints: Int
- returns
the number of constraints in the solver.
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def
numberOfVariables: Int
- returns
the number of variables in the solver.
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val
numberOfVars: Int
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-
def
objectiveValue: Option[Double]
- returns
the objective value of the solution found by the solver
- Note
The objective value may be the best feasible solution if optimality is not attained or proven.
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def
setDoubleUnbounded(colId: Int): Unit
Set bot upper and lower bounds to unbounded (infinite).
Set bot upper and lower bounds to unbounded (infinite).
- colId
position of the variable
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def
solution: Array[Double]
- returns
the solution found by the solver, for each variable
- Note
the solution may be the best feasible solution found so far if optimality is not attained or proven.
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def
solutionStatus: SolutionStatus
- returns
the status of the solution found by the solver
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def
synchronized[T0](arg0: ⇒ T0): T0
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wait(arg0: Long, arg1: Int): Unit
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wait(arg0: Long): Unit
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wait(): Unit
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