搜索资源列表
homework-code
- 不精确一维搜索wolfe-Powell准则、BFGS、牛顿法、LU分解、有效集法等-Inexact line search wolfe-Powell criteria, BFGS, Newton' s method, LU decomposition, the effective collection method
BFGS-youhua
- 用于解决优化问题中的无约束优化,经过检测，能够成功运行。-To solve the problem of unconstrained optimization.
sourcefile
- 最优化方法，作业，包括BFGS，SUMT惩罚函数法等-Optimization
BFGS
- 最优化方法作业 BFGS方法 有算法分析 步骤 实例 实验结果和C++源程序-Operations optimization algorithm analysis step BFGS method is an instance of experimental results and the C++ source code
bfgswopt
- steepest descent/bfgs with polynomial line search
multidimensional-extremum-problems
- 无约束多维极值问题，包含 用模式搜索法求解多维函数的极值 用Rosenbrock法求解多维函数的极值 用单纯形搜索法求解多维函数的极值 用Powell法求解多维函数的极值 用最速下降法求解多维函数的极值 用共轭梯度法求解多维函数的极 用牛顿法求解多维函数的极值 用修正牛顿法求解多维函数的极值 用DFP法求解多维函数的极值 用BFGS法求解多维函数的极值 用信赖域法求解多维函数的极值 用显式最速下降法求正定二次函数的极值 -Unconstrain
3
- BFGS拟牛顿法求非线性无约束最优化（函数极值）问题-BFGS quasi-Newton method for solving nonlinear unconstrained optimization (function extremum) problem
BFGS
- 用变尺度法（BFGS）寻找方程最优解，vbs实现，win rt可用。-Looking equations with BFGS optimal solution, vbs achieve, win rt available.
bfgsopt
- BFGS法求多元函数的极小值，用迭代方式进行线性搜索。-The minimal value of multivariate function by BFGS method
minBFGS
- 用BFGS法求解多维函数的极值 function [x,minf]=minBFGS(f,x0,var,eps) 目标函数：f 初始点：x0 自变量向量：var 精度：eps 目标函数取最小值时的自变量值：x 目标函数的最小值：minf- By BFGS method for solving the multi-dimensional function of the extreme value function [x, minf] = minBFGS (
BFGS
- 优化算法，选取最优点,这是一个纯数值的算法，给出一个初始点，进行最优点的选取-optimization algorithm
bayesian-hill-climbin
- FORTRAN code for minimizing a function whose uation is expensive. At each iteration, a Bayesian posterior mean for the surface shape conditional on points already sampled is constructed and the minimum of this is found. This minimum is then used as
BFGS
- BFGS算法的实现(Implementation of BFGS algorithm)
优化算法
- 解决了最小无约束优化问题 步长由ARmijo非精确一维搜索生成，迭代方向分别由最速下降法，阻尼牛顿法，共轭梯度法，拟牛顿法(BFGS)产生(This code solves the minimum unconstrained optimization problem, and the step size is generated by ARmijo inexact one-dimensional search. The iterative directions are generated b
L-BFGS-B-C-master
- 基于梯度下降法的最优迭代算法，在深度学习和神经网络中应用非常广泛，也非常好用(The optimal iterative algorithm based on gradient descent method is widely used in depth learning and neural network, and it is also very useful.)
第一题bfgs
- armijo准则下bfgs法实现，最优化方法作业，大连理工大学(Realization of BFGS method under Armijo criterion, operation of optimization method, Dalian University of Technology)