simplex-method
simplex-method
is a Haskell library that implements the two-phase simplex method in exact rational arithmetic.
Quick Overview
The Linear.Simplex.Solver.TwoPhase
module contain both phases of the two-phase simplex method.
Phase One
Phase one is implemented by findFeasibleSolution
:
findFeasibleSolution :: (MonadIO m, MonadLogger m) => [PolyConstraint] -> m (Maybe FeasibleSystem)
findFeasibleSolution
takes a list of PolyConstraint
s.
The PolyConstraint
type, as well as other custom types required by this library, are defined in the Linear.Simplex.Types
module.
PolyConstraint
is defined as:
data PolyConstraint
= LEQ {lhs :: VarLitMapSum, rhs :: SimplexNum}
| GEQ {lhs :: VarLitMapSum, rhs :: SimplexNum}
| EQ {lhs :: VarLitMapSum, rhs :: SimplexNum}
deriving (Show, Read, Eq, Generic)
SimplexNum
is an alias for Rational
, and VarLitMapSum
is an alias for VarLitMap
, which is an alias for Map Var SimplexNum
.
Var
is an alias of Int
.
A VarLitMapSum
is read as Integer
variables mapped to their Rational
coefficients, with an implicit +
between each entry.
For example: Map.fromList [(1, 2), (2, (-3)), (1, 3)]
is equivalent to (2x1 + (-3x2) + 3x1)
.
And a PolyConstraint
is an inequality/equality where the LHS is a VarLitMapSum
and the RHS is a Rational
.
For example: LEQ (Map.fromList [(1, 2), (2, (-3)), (1, 3)] 60)
is equivalent to (2x1 + (-3x2) + 3x1) <= 60
.
Passing a [PolyConstraint]
to findFeasibleSolution
will return a FeasibleSystem
if a feasible solution exists:
data FeasibleSystem = FeasibleSystem
{ dict :: Dict
, slackVars :: [Var]
, artificialVars :: [Var]
, objectiveVar :: Var
}
deriving (Show, Read, Eq, Generic)
type Dict = M.Map Var DictValue
data DictValue = DictValue
{ varMapSum :: VarLitMapSum
, constant :: SimplexNum
}
deriving (Show, Read, Eq, Generic)
Dict
can be thought of as a set of equations, where the key represents a basic variable on the LHS of the equation
that is equal to the RHS represented as a DictValue
value.
Phase Two
optimizeFeasibleSystem
performs phase two of the simplex method, and has the type:
optimizeFeasibleSystem :: (MonadIO m, MonadLogger m) => ObjectiveFunction -> FeasibleSystem -> m (Maybe Result)
data ObjectiveFunction = Max {objective :: VarLitMapSum} | Min {objective :: VarLitMapSum}
data Result = Result
{ objectiveVar :: Var
, varValMap :: VarLitMap
}
deriving (Show, Read, Eq, Generic)
We give optimizeFeasibleSystem
an ObjectiveFunction
along with a FeasibleSystem
.
Two-Phase Simplex
twoPhaseSimplex
performs both phases of the simplex method.
It has the type:
twoPhaseSimplex :: (MonadIO m, MonadLogger m) => ObjectiveFunction -> [PolyConstraint] -> m (Maybe Result)
The result of the objective function is present in the returned Result
type of both twoPhaseSimplex
and optimizeFeasibleSystem
, but this can be difficult to grok in systems with many variables, so the following function will extract the value of the objective function for you.
dictionaryFormToTableau :: Dict -> Tableau
There are similar functions for DictionaryForm
as well as other custom types in the module Linear.Simplex.Util
.
Example
exampleFunction :: (ObjectiveFunction, [PolyConstraint])
exampleFunction =
(
Max {objective = Map.fromList [(1, 3), (2, 5)]}, -- 3x1 + 5x2
[
LEQ {lhs = Map.fromList [(1, 3), (2, 1)], rhs = 15}, -- 3x1 + x2 <= 15
LEQ {lhs = Map.fromList [(1, 1), (2, 1)], rhs = 7}, -- x1 + x2 <= 7
LEQ {lhs = Map.fromList [(2, 1)], rhs = 4}, -- x2 <= 4
LEQ {lhs = Map.fromList [(1, -1), (2, 2)], rhs = 6} -- -x1 + 2x2 <= 6
]
)
twoPhaseSimplex (fst exampleFunction) (snd exampleFunction)
The result of the call above is:
Just
(Result
{ objectiveVar = 7 -- Integer representing objective function
, varValMap = Map.fromList
[ (7, 29) -- Value for variable 7, so max(3x1 + 5x2) = 29.
, (1, 3) -- Value for variable 1, so x1 = 3
, (2, 4) -- Value for variable 2, so x2 = 4
]
}
)
There are many more examples in test/TestFunctions.hs.
You may use prettyShowVarConstMap
, prettyShowPolyConstraint
, and prettyShowObjectiveFunction
to convert these tests into a more human-readable format.
Issues
Please share any bugs you find here.