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Packages that use Uncmin_methods | |
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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 |
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Classes in gametheory that implement Uncmin_methods | |
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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 |
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Methods in newtonSolver with parameters of type Uncmin_methods | |
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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. |
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