breeze.optimize.linear

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Simple LP solver based on http://en.wikipedia.org/wiki/Karmarkar's_algorithm Note that this is not Karmarkar's algorithm.

Simple LP solver based on http://en.wikipedia.org/wiki/Karmarkar's_algorithm Note that this is not Karmarkar's algorithm.

Companion:
object
Companion:
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Algorithms for finding a bipartite matching. We include one optimal algorithm (KuhnMunkres) and one greedy algorithm (competitive linking).

Algorithms for finding a bipartite matching. We include one optimal algorithm (KuhnMunkres) and one greedy algorithm (competitive linking).

Algorithms find minimum matchings.

mixed 0-1 ILP Solver based on Branch and bound

mixed 0-1 ILP Solver based on Branch and bound

See also:
class ConjugateGradient[T, M](maxNormValue: Double, maxIterations: Int, normSquaredPenalty: Double, tolerance: Double)(implicit space: MutableInnerProductVectorSpace[T, Double], mult: Impl2[M, T, T]) extends SerializableLogging

Solve argmin (a dot x + .5 * x dot (B * x) + .5 * normSquaredPenalty * (x dot x)) for x subject to norm(x) <= maxNormValue

Solve argmin (a dot x + .5 * x dot (B * x) + .5 * normSquaredPenalty * (x dot x)) for x subject to norm(x) <= maxNormValue

Based on the code from "Trust Region Newton Method for Large-Scale Logistic Regression"

  • @author dlwh
case class InfeasibleProblem(prob: Problem) extends RuntimeException

InteriorPoint solver for LPs.

InteriorPoint solver for LPs.

Algorithm to find a minimum cost matching on a bipartite graph.

Algorithm to find a minimum cost matching on a bipartite graph.

Implements the hungarian algorithm.

DSL for LinearPrograms. Not thread-safe per instance. Make multiple instances

DSL for LinearPrograms. Not thread-safe per instance. Make multiple instances

Basic example:

val lp = new LP
import lp._
val x = new Positive("x")
val y = new Positive("y")

val result = maximize ( (3 * x+ 4 * y)
subjectTo( x <= 3, y <= 1))

result.valueOf(x) // 3

Companion:
object
Companion:
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class NNLS(val maxIters: Int) extends SerializableLogging

NNLS solves nonnegative least squares problems using a modified projected gradient method.

NNLS solves nonnegative least squares problems using a modified projected gradient method.

Value parameters:
maxIters

user defined maximum iterations

Companion:
object
object NNLS
Companion:
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class PowerMethod(maxIters: Int, tolerance: Double) extends SerializableLogging

Power Method to compute maximum eigen value and companion object to compute minimum eigen value through inverse power iterations

Power Method to compute maximum eigen value and companion object to compute minimum eigen value through inverse power iterations

Companion:
object
Companion:
class