Uses of Interface
newtonSolver.Uncmin_methods

Packages that use Uncmin_methods
gametheory Contains code for estimating the entry into auctions application in Bajari, Hong, and Ryan (2009). 
newtonSolver Slightly modified version of Steve Verrill's Java translation of several FORTRAN optimization routines, found here
 

Uses of Uncmin_methods in gametheory
 

Classes in gametheory that implement Uncmin_methods
 class GameMinimization
          Class to perform the optimization of the GMM function on real auction entry data.
 class GameMinimizationPrivateInformation
          Private information version of discrete game used to find starting values in the complete information game.
 

Uses of Uncmin_methods in newtonSolver
 

Methods in newtonSolver with parameters of type Uncmin_methods
 void Uncmin_f77.dogdrv_f77(int n, double[] x, double[] f, double[] g, double[][] a, double[] p, double[] xpls, double[] fpls, Uncmin_methods minclass, double[] sx, double[] stepmx, double[] steptl, double[] dlt, int[] iretcd, boolean[] mxtake, double[] sc, double[] wrk1, double[] wrk2, double[] wrk3)
           The dogdrv_f77 method finds the next Newton iterate (xpls) by the double dogleg method.
 void Uncmin_f77.fstocd_f77(int n, double[] x, Uncmin_methods minclass, double[] sx, double rnoise, double[] g)
           The fstocd_f77 method finds a central difference approximation to the gradient of the function to be minimized.
 void Uncmin_f77.fstofd_f77(int n, double[] xpls, Uncmin_methods minclass, double[] fpls, double[][] a, double[] sx, double rnoise, double[] fhat)
           This version of the fstofd_f77 method finds a finite difference approximation to the Hessian.
 void Uncmin_f77.fstofd_f77(int n, double[] xpls, Uncmin_methods minclass, double[] fpls, double[] g, double[] sx, double rnoise)
           This version of the fstofd_f77 method finds first order finite difference approximations for gradients.
 void Uncmin_f77.grdchk_f77(int n, double[] x, Uncmin_methods minclass, double[] f, double[] g, double[] typsiz, double[] sx, double[] fscale, double rnf, double analtl, double[] gest)
           The grdchk_f77 method checks the analytic gradient supplied by the user.
 void Uncmin_f77.heschk_f77(int n, double[] x, Uncmin_methods minclass, double[] f, double[] g, double[][] a, double[] typsiz, double[] sx, double rnf, double analtl, int[] iagflg, double[] udiag, double[] wrk1, double[] wrk2)
           The heschk_f77 method checks the analytic Hessian supplied by the user.
 void Uncmin_f77.hookdr_f77(int n, double[] x, double[] f, double[] g, double[][] a, double[] udiag, double[] p, double[] xpls, double[] fpls, Uncmin_methods minclass, double[] sx, double[] stepmx, double[] steptl, double[] dlt, int[] iretcd, boolean[] mxtake, double[] amu, double[] dltp, double[] phi, double[] phip0, double[] sc, double[] xplsp, double[] wrk0, double epsm, int[] itncnt)
           The hookdr_f77 method finds a next Newton iterate (xpls) by the More-Hebdon technique.
 void Uncmin_f77.lnsrch_f77(int n, double[] x, double[] f, double[] g, double[] p, double[] xpls, double[] fpls, Uncmin_methods minclass, boolean[] mxtake, int[] iretcd, double[] stepmx, double[] steptl, double[] sx)
           The lnsrch_f77 method finds a next Newton iterate by line search.
 void Uncmin_f77.optdrv_f77(int n, double[] x, Uncmin_methods minclass, double[] typsiz, double[] fscale, int[] method, int[] iexp, int[] msg, int[] ndigit, int[] itnlim, int[] iagflg, int[] iahflg, double[] dlt, double[] gradtl, double[] stepmx, double[] steptl, double[] xpls, double[] fpls, double[] gpls, int[] itrmcd, double[][] a, double[] udiag, double[] g, double[] p, double[] sx, double[] wrk0, double[] wrk1, double[] wrk2, double[] wrk3)
           The optdrv_f77 method is the driver for the nonlinear optimization problem.
 void Uncmin_f77.optif0_f77(int n, double[] x, Uncmin_methods minclass, double[] xpls, double[] fpls, double[] gpls, int[] itrmcd, double[][] a, double[] udiag)
           The optif0_f77 method minimizes a smooth nonlinear function of n variables.
 void Uncmin_f77.optif9_f77(int n, double[] x, Uncmin_methods minclass, double[] typsiz, double[] fscale, int[] method, int[] iexp, int[] msg, int[] ndigit, int[] itnlim, int[] iagflg, int[] iahflg, double[] dlt, double[] gradtl, double[] stepmx, double[] steptl, double[] xpls, double[] fpls, double[] gpls, int[] itrmcd, double[][] a, double[] udiag)
           The optif9_f77 method minimizes a smooth nonlinear function of n variables.
 void Uncmin_f77.sndofd_f77(int n, double[] xpls, Uncmin_methods minclass, double[] fpls, double[][] a, double[] sx, double rnoise, double[] stepsz, double[] anbr)
           The sndofd_f77 method finds second order forward finite difference approximations to the Hessian.
 void Uncmin_f77.tregup_f77(int n, double[] x, double[] f, double[] g, double[][] a, Uncmin_methods minclass, double[] sc, double[] sx, boolean[] nwtake, double[] stepmx, double[] steptl, double[] dlt, int[] iretcd, double[] xplsp, double[] fplsp, double[] xpls, double[] fpls, boolean[] mxtake, int method, double[] udiag)
           The tregup_f77 method decides whether to accept xpls = x + sc as the next iterate and update the trust region dlt.