Uses of Class
jline.io.Ret.pfqnNc

Packages that use Ret.pfqnNc

Package	Description
jline.api.pfqn.ld	Load-dependent Product Form Queueing Network algorithms.
jline.api.pfqn.nc	Normalizing constant algorithms for Product Form Queueing Networks.

Uses of Ret.pfqnNc in jline.api.pfqn.ld

Methods in jline.api.pfqn.ld that return Ret.pfqnNc

Modifier and Type	Method	Description
static Ret.pfqnNc	Pfqn_ncld.compute_norm_const_ld(Matrix L, Matrix N, Matrix Z, Matrix mu, SolverOptions options)
static Ret.pfqnNc	Pfqn_gld.pfqn_gld(Matrix L, Matrix N, Matrix mu, SolverOptions options)	Compute the normalizing constant of a single-class load-dependent closed queueing network model
static Ret.pfqnNc	Pfqn_gldsingle.pfqn_gldsingle(Matrix L, Matrix N, Matrix mu, SolverOptions options)	Auxiliary function used by pfqn_gld to compute the normalizing constant in a single-class load-dependent model.
static Ret.pfqnNc	Pfqn_lld.pfqn_lld(Matrix L, Matrix N, Matrix mu, SolverOptions options)	Normalizing constant of a multiclass limited load-dependent closed model.
static Ret.pfqnNc	Pfqn_lldsingle.pfqn_lldsingle(Matrix L, Matrix N, Matrix mu, SolverOptions options)	Auxiliary function used by Pfqn_ncld and Pfqn_nre to compute the normalizing constant in a single-class limited load-dependent model.
static Ret.pfqnNc	Pfqn_ncld.pfqn_ncld(Matrix L, Matrix N, Matrix Z, Matrix mu, SolverOptions options)
static Ret.pfqnNc	Pfqn_panaceald.pfqn_panaceald(Matrix L, Matrix N, Matrix Z, Matrix mu)
static Ret.pfqnNc	Pfqn_panaceald.pfqn_panaceald(Matrix L, Matrix N, Matrix Z, Matrix mu, int terms)	Compute the load-dependent PANACEA approximation

Uses of Ret.pfqnNc in jline.api.pfqn.nc

Methods in jline.api.pfqn.nc that return Ret.pfqnNc

Modifier and Type	Method	Description
static Ret.pfqnNc	Pfqn_aghq.pfqn_aghq(Matrix L, Matrix N)
static Ret.pfqnNc	Pfqn_aghq.pfqn_aghq(Matrix L, Matrix N, Matrix Z)
static Ret.pfqnNc	Pfqn_aghq.pfqn_aghq(Matrix L, Matrix N, Matrix Z, int q)
static Ret.pfqnNc	Pfqn_bk.pfqn_bk(Matrix L, Matrix N, Matrix Z)	Birman-Kogan saddle point normalizing constant with bottleneck detection.
static Ret.pfqnNc	Pfqn_bkt.pfqn_bkt(Matrix L, Matrix N)
static Ret.pfqnNc	Pfqn_bkt.pfqn_bkt(Matrix L, Matrix N, Matrix Z)	Knessl-Tier expansion corrected for the Stirling remainder that steepest descent drops in each class direction.
static Ret.pfqnNc	Pfqn_bk.pfqn_bkue(Matrix L, double N, double Z)	Birman-Kogan uniform (van der Waerden) expansion for a single chain.
static Ret.pfqnNc	Pfqn_ble.pfqn_ble(Matrix L, Matrix N)
static Ret.pfqnNc	Pfqn_ble.pfqn_ble(Matrix L, Matrix N, Matrix Z)	Logistic expansion with an additive (1-log(2*pi)/2) bias correction per Gaussian direction the branch actually takes.
static Ret.pfqnNc	Pfqn_ca.pfqn_ca(Matrix L, Matrix N)
static Ret.pfqnNc	Pfqn_ca.pfqn_ca(Matrix L, Matrix N, Matrix Z)
static Ret.pfqnNc	Pfqn_clw.pfqn_clw(Matrix L, Matrix N)
static Ret.pfqnNc	Pfqn_clw.pfqn_clw(Matrix L, Matrix N, Matrix Z)
static Ret.pfqnNc	Pfqn_clw.pfqn_clw(Matrix L, Matrix N, Matrix Z, Matrix m)
static Ret.pfqnNc	Pfqn_clw.pfqn_clw(Matrix L, Matrix N, Matrix Z, Matrix m, Matrix lpar, Matrix gampar)
static Ret.pfqnNc	Pfqn_clw.pfqn_clw(Matrix L, Matrix N, Matrix Z, Matrix m, Matrix lpar, Matrix gampar, Pfqn_clw.Options opt)
static Ret.pfqnNc	Pfqn_clw_lld.pfqn_clw_lld(Matrix L, Matrix N)
static Ret.pfqnNc	Pfqn_clw_lld.pfqn_clw_lld(Matrix L, Matrix N, Matrix Z)
static Ret.pfqnNc	Pfqn_clw_lld.pfqn_clw_lld(Matrix L, Matrix N, Matrix Z, Matrix mu)
static Ret.pfqnNc	Pfqn_clw_lld.pfqn_clw_lld(Matrix L, Matrix N, Matrix Z, Matrix mu, Matrix lpar, Matrix gampar)
static Ret.pfqnNc	Pfqn_clwjd.pfqn_clwjd(double[] Z, int[] N, List<ToDoubleFunction<int[]>> mu)
static Ret.pfqnNc	Pfqn_clwjd.pfqn_clwjd(double[] Z, int[] N, List<ToDoubleFunction<int[]>> mu, double[][] visits)
static Ret.pfqnNc	Pfqn_clwjd.pfqn_clwjd(double[] Z, int[] N, List<ToDoubleFunction<int[]>> mu, double[][] visits, int[][] lcut)
static Ret.pfqnNc	Pfqn_clwjd.pfqn_clwjd(double[] Z, int[] N, List<ToDoubleFunction<int[]>> mu, double[][] visits, int[][] lcut, int[] lpar, double[] gampar)
static Ret.pfqnNc	Pfqn_clwoi.pfqn_clwoi(double[] Z, int[] N, List<ToDoubleFunction<int[]>> mu)
static Ret.pfqnNc	Pfqn_clwoi.pfqn_clwoi(double[] Z, int[] N, List<ToDoubleFunction<int[]>> mu, double[][] visits)
static Ret.pfqnNc	Pfqn_clwoi.pfqn_clwoi(double[] Z, int[] N, List<ToDoubleFunction<int[]>> mu, double[][] visits, int[] lpar, double[] gampar)
static Ret.pfqnNc	Pfqn_cub.pfqn_cub(Matrix L, Matrix N, Matrix Z)	Cubature method to compute the normalizing constant of a load-independent closed queueing network model
static Ret.pfqnNc	Pfqn_cub.pfqn_cub(Matrix L, Matrix N, Matrix Z, int order, double atol)	Cubature method to compute the normalizing constant of a load-independent closed queueing network model
static Ret.pfqnNc	Pfqn_gerasimov.pfqn_gerasimov(Matrix L, Matrix N)	Delay-free case.
static Ret.pfqnNc	Pfqn_gerasimov.pfqn_gerasimov(Matrix L, Matrix N, Matrix Z)	Defaults: tol = 1e-12, maxterms = 200000.
static Ret.pfqnNc	Pfqn_gerasimov.pfqn_gerasimov(Matrix L, Matrix N, Matrix Z, double tol, int maxterms)	Exact normalizing constant of a closed multiclass product-form network by ITERATED RESIDUES of its rational generating function, one class at a time.
static Ret.pfqnNc	Pfqn_is.pfqn_is(Matrix L, Matrix N, Matrix Z, SolverOptions options)	Importance-sampling estimate of the load-independent normalizing constant.
static Ret.pfqnNc	Pfqn_kt.pfqn_kt(Matrix L, Matrix N, Matrix Z)	Knessl-Tier asymptotic expansion of the normalizing constant using the ray method.
static Ret.pfqnNc	Pfqn_ld_is.pfqn_ld_is(Matrix L, Matrix N, Matrix Z, Matrix mu, SolverOptions options)	Importance-sampling estimate of the load-dependent normalizing constant.
static Ret.pfqnNc	Pfqn_le.pfqn_le(Matrix L, Matrix N)
static Ret.pfqnNc	Pfqn_le.pfqn_le(Matrix L, Matrix N, Matrix Z)	Logistic expansion method to compute the normalizing constant.
static Ret.pfqnNc	Pfqn_lekt.pfqn_lekt(Matrix L, Matrix N)
static Ret.pfqnNc	Pfqn_lekt.pfqn_lekt(Matrix L, Matrix N, Matrix Z)	The corrected logistic expansion (pfqn_ble) and the corrected Knessl-Tier expansion (pfqn_bkt) are ONE estimator, evaluated in M-1 and in R dimensions: with a think time their stationary points are one point in dual coordinates (xi_r = N_r/(Z_r + v u'L_r), the class throughputs of the LE fixed point, and v u_k = 1/(1-U_k), the M/M/1 factor of the KT saddle) and Sylvester's identity exchanges the R x R Hessian determinant for the M x M one, after which every 2 pi cancels.
static Ret.pfqnNc	Pfqn_ls.pfqn_ls(Matrix L, Matrix N, long I)
static Ret.pfqnNc	Pfqn_ls.pfqn_ls(Matrix L, Matrix N, long I, long seed)
static Ret.pfqnNc	Pfqn_ls.pfqn_ls(Matrix L, Matrix N, Matrix Z, long I)
static Ret.pfqnNc	Pfqn_ls.pfqn_ls(Matrix L, Matrix N, Matrix Z, long I, long seed)	Logistic sampling method to compute the normalizing constant.
static Ret.pfqnNc	Pfqn_mci.pfqn_mci(Matrix D, Matrix N, Matrix Z, int I, String variant)	Monte Carlo integration for the normalizing constant.
static Ret.pfqnNc	Pfqn_mmint2.pfqn_mmint2(Matrix L, Matrix N, Matrix Z)
static Ret.pfqnNc	Pfqn_mmint2_gausslaguerre.pfqn_mmint2_gausslaguerre(Matrix L, Matrix N, Matrix Z)
static Ret.pfqnNc	Pfqn_mmint2_gausslaguerre.pfqn_mmint2_gausslaguerre(Matrix L, Matrix N, Matrix Z, int m)
static Ret.pfqnNc	Pfqn_mmint2_gausslegendre.pfqn_mmint2_gausslegendre(Matrix L, Matrix N, Matrix Z, Integer m)	Compute the normalizing constant of a repairmen model using Gauss-Legendre integration.
static Ret.pfqnNc	Pfqn_mmsample2.pfqn_mmsample2(Matrix L, Matrix N, Matrix Z, int samples)
static Ret.pfqnNc	Pfqn_panacea.pfqn_panacea(Matrix L, Matrix N, Matrix Z)
static Ret.pfqnNc	Pfqn_panacea.pfqn_panacea(Matrix L, Matrix N, Matrix Z, SolverOptions options)	Compute the PANACEA approximation
static Ret.pfqnNc	Pfqn_recal.pfqn_recal(Matrix L, Matrix N)	RECAL method to compute the normalizing constant of a load-independent closed queueing network model.
static Ret.pfqnNc	Pfqn_recal.pfqn_recal(Matrix L, Matrix N, Matrix Z)
static Ret.pfqnNc	Pfqn_recal.pfqn_recal(Matrix L, Matrix N, Matrix Z, Matrix m0)