Package jline.api.mapqn

Class Mapqn_qrf_noblo_bethe

java.lang.Object

jline.api.mapqn.Mapqn_qrf_noblo_bethe

public final class Mapqn_qrf_noblo_bethe
extends Object

Method Summary

Modifier and Type	Method	Description
static Mapqn_solution	solve(int M, int MR_input, int[] K, int N, double[][][] mu, double[][][] v, double[][] rt)	Solve the no-blocking quadratic reduction under the tree-reweighted free entropy.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Details

solve

public static Mapqn_solution solve(int M,
 int MR_input,
 int[] K,
 int N,
 double[][][] mu,
 double[][][] v,
 double[][] rt)

Solve the no-blocking quadratic reduction under the tree-reweighted free entropy.

The polytope, the phase-1 feasible start and the NLP call are exactly those of Mapqn_qrf_noblo_mmi.solve(int, int, int[], int, double[][][], double[][][], double[][]); the objective is the only difference. See Mapqn_qrf_noblo_mmi.betheObjective(double[], int, int, int[], int, int[], int) for what it is and why the weight is lambda = 1/M.

ONE SOLVE, NO RESTARTS. The objective is convex on this polytope, so there is no second local minimum for a restart to find; the single solve from the phase-1 point returns the global optimum.

Parameters:

M - number of queues

MR_input - ignored -- no blocking means one configuration, by definition

K - phases per queue

N - total population

mu - completion rates, [M][Kmax][Kmax]

v - background rates, [M][Kmax][Kmax]

rt - routing matrix, [M][M]