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edu.berkeley.ce.sparkrocks

LinearProgram

Related Docs: object LinearProgram | package sparkrocks

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class LinearProgram extends AnyRef

A linear program. Maximizes or minimizes the function <c,x> subject to the constraint Ax {<.=,>} b, where b, c, and x are vectors and A is a matrix.

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new LinearProgram(numVars: Int)

Initialize a new linear program.
Initialize a new linear program.
numVars
The number of variables in the linear program.

Value Members

final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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def addConstraint(coeffs: Array[Double], operator: Operator, rhs: Double): Unit

Add a new constraint to the linear program.
Add a new constraint to the linear program.
coeffs
The coefficients of the variables for the constraint. If the number of entries exceeds the number of variables in the LP, the extra entries are ignored. If the number of entries is less than the number of variables, coefficients of 0.0 are added by default.
operator
One of LE, for <=, EQ for ==, or GE for =>.
rhs
The right-hand side of the constraint.
final def asInstanceOf[T0]: T0

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def clone(): AnyRef

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protected[java.lang]
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@throws( ... )
val constraints: ArrayBuffer[LinearConstraint]
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def finalize(): Unit

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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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var goalType: Option[GoalType]
def hashCode(): Int

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final def isInstanceOf[T0]: Boolean

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final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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var objectiveFunction: Option[LinearObjectiveFunction]
def setObjFun(coeffs: Array[Double], objType: ObjectiveType): Unit

Set the objective function of the linear program.
Set the objective function of the linear program.
coeffs
The coefficients of the variables in the objective function, i.e. the entries of c. If the number of entries exceeds the number of variables, the extra entries are ignored. If the number of entries is less than the number of variables, coefficients of 0.0 are added by default.
objType
One of either MIN (to minimize) or MAX (to maximize).
def solve(): Option[(Array[Double], Double)]

Solve the linear program to compute the optimal objective value.
Solve the linear program to compute the optimal objective value. Note that this instance becomes unuseable after this method completes.
returns
None if an error occurred, otherwise Some((varSettings, opt)), where varSettings is the value assigned to each value to optimize the objective function and opt is the optimal objective value itself.
final def synchronized[T0](arg0: ⇒ T0): T0

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def toString(): String

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final def wait(): Unit

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final def wait(arg0: Long, arg1: Int): Unit

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final def wait(arg0: Long): Unit

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