搜索资源列表
min
- 无约束多维问题的m文件程序，包括鲍威尔法，单纯性法，最速下降法，BFGS方法等-Unconstrained multidimensional problem of m document procedures, including Powell method, simplex method, steepest descent method, BFGS method
linear_CTSSVM
- 基于分段函数的支持向量分类机，使用BFGS算法进行求解-Piecewise-based support vector machine, using BFGS algorithm to solve
a2
- main algorithm bfgs and ls and zoom
Optimization
- .net 图片播放 数值优化，bfgs，fdp，共轭梯度法，黄金分割法-.net picture playback numerical optimization, bfgs, fdp, conjugate gradient method, golden section method
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
matlab
- QAM调制，最速下降法，Newton，黄金分割法， BFGS算法，非线性共轭梯度法，MAILAB实例-QAM modulation, the steepest descent method, Newton, golden section method, BFGS algorithm, nonlinear conjugate gradient method, MAILAB examples
nonlinear-optimization
- 非线性规划各种算法汇总，包括线搜索、梯度下降法、牛顿法、共轭梯度法、DFP算法、BFGS算法和信赖域算法-Summary all kinds of algorithm of nonlinear programming, including line search, the gradient descent method, Newton method and conjugate gradient method, DFP algorithm and BFGS algorithm and trust
lbfgs
- L-BFGS算法比较适合在大规模的数值计算中，具备牛顿法收敛速度快的特点，但不需要牛顿法那样存储Hesse矩阵，因此节省了大量的空间以及计算资源。-Limited-memory BFGS (L-BFGS or LM-BFGS) is an optimization algorithm in the family of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorit
quasi-Newton-method
- 功能：用BFGS算法求解无约束问题：min f(x) 输入：x0是初始点，fun,gfun分别是目标函数及其梯度； varargin是输入的可变参数变量，简单调用bfgs时可以忽略它 但若其他程序循环调用该程序时将发生重要作用-Function: with BFGS algorithm solving unconstrained problem: min f (x) input: x0 is the initial point, fun, gfun respective
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
An-Improved-Learning-Algorithm-Based-on-BFGS-Meth
- This paper suggests that a simple modification to the initial search direction can also substantially improve the training efficiency of almost all major optimization methods. It was discovered that if the initial search direction is locally modified
BFGS
- 拟牛顿法和最速下降法(Steepest Descent Methods)一样只要求每一步迭代时知道目标函数的梯度。通过测量梯度的变化，构造一个目标函数的模型使之足以产生超线性收敛性。这类方法大大优于最速下降法，尤其对于困难的问题。另外，因为拟牛顿法不需要二阶导数的信息，所以有时比牛顿法(Newton s Method)更为有效。如今，优化软件中包含了大量的拟牛顿算法用来解决无约束，约束，和大规模的优化问题。-The quasi-Newton method and the Steepest Des
最优化
- 中国石油大学（华东）最优化课程要求的两个作业程序：乘子法和Armiyo算法+BFGS算法(China University of Petroleum (East China) optimization course requirements of the two operating procedures: multiplier and Armiyo algorithm, +BFGS algorithm)
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782290
- 无约束优化DFP&BFGS方法的C++实现()
749753
- 无约束优化DFP&BFGS方法的C++实现()
Deep-ADMM-Net-master
- Net is defined over a data flow graph, which is derived from the iterative pro- cedures in Alternating Direction Method of Multipliers (ADMM) algorithm for optimizing a CS-based MRI model. In the training phase, all parameters of the net, e.g., im
niuduanlafuxun12
- 电力系统潮流计算包含11个节点并能生成相关参数图形(Matlab for power system)
Newton
- 求解无约束最优化问题，Newton方法包括基本Newton法，拟Newton法等BFGS，DFP方法(Solving unconstrained optimization problems, Newton method)
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.)