代码搜索：differentiable
找到约 11 项符合「differentiable」的源代码
代码结果 11
www.eeworm.com/read/434325/7874750
m a1algos.m
%---------------------------------------------------------------------------
%A1ALGOS
%
% NUMERICAL METHODS: MATLAB Programs, (c) John H. Mathews 1995
% To accompany the text:
% NUMERICAL METHODS
www.eeworm.com/read/304833/13785306
dif_
differ from,与…不同
differ,v.不同;相争
difference,n.差异;差异点
different from,和…不同
different,adj.不同的;差异的
differentia,n.不同点;差异
differentiability,n.可辨性
differentiable,adj.可辨的;可区分的
differentiae,n.differen
www.eeworm.com/read/347945/11625388
m nonlinopex.m
clc
echo on
% A recent and devloping extension in YALMIP is support
% for nonlinear operators such as min, max, norm and more.
%
% Although nonlinear, and often non-differentiable, the resultin
www.eeworm.com/read/473219/6849064
m nonlinopex.m
clc
echo on
% A recent and devloping extension in YALMIP is support
% for nonlinear operators such as min, max, norm and more.
%
% Although nonlinear, and often non-differentiable, the resultin
www.eeworm.com/read/193277/8243077
m nonlinopex.m
clc
echo on
% A recent and devloping extension in YALMIP is support
% for nonlinear operators such as min, max, norm and more.
%
% Although nonlinear, and often non-differentiable, the resultin
www.eeworm.com/read/199528/5076171
java difffunction.java
package edu.stanford.nlp.optimization;
/**
* An interface for once-differentiable double-valued functions over
* double arrays. NOTE: it'd be good to have an AbstractDiffFunction
* that wrapped a
www.eeworm.com/read/199528/5076169
java cgminimizer.java
package edu.stanford.nlp.optimization;
/**
* Conjugate-gradient implementation based on the code in Numerical
* Recipes in C. (See p. 423 and others.) As of now, it requires a
* differentiable f
www.eeworm.com/read/247625/12638850
txt readme.txt
Orthant-Wise Limited-memory Quasi-Newton algorithm minimizes functions of the form
f(w) = loss(w) + C |w|_1
where loss is an arbitrary differentiable convex loss function, and |w|_1 is the L
www.eeworm.com/read/281979/9125418
m conjugate gradient minimization -minimize.m
function [X, fX, i] = minimize(X, f, length, varargin)
% Minimize a differentiable multivariate function.
%
% Usage: [X, fX, i] = minimize(X, f, length, P1, P2, P3, ... )
%
% where the starting poin
www.eeworm.com/read/469123/6977822
m minimize.m
function [X, fX, i] = minimize(X, f, length, varargin)
% Minimize a differentiable multivariate function.
%
% Usage: [X, fX, i] = minimize(X, f, length, P1, P2, P3, ... )
%
% where the starting poin