Type Members

1.  class AffineScaling extends SerializableLogging

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

2.  trait BipartiteMatching extends AnyRef

   Algorithms for finding a bipartite matching.

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

   Algorithms find minimum matchings.

3.  class ConjugateGradient[T, M] 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

4.  case class InfeasibleProblem(prob: Problem) extends RuntimeException with Product with Serializable
5.  class LinearProgram extends AnyRef

   DSL for LinearPrograms.

   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

6.  class NNLS extends SerializableLogging

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

7.  class PowerMethod extends SerializableLogging

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