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Limited-memory BFGS - Wikipedia

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 algorithm (BFGS) using a limited amount …

En.wikipedia.org

DA: 16 PA: 25 MOZ Rank: 41

BFGS算法 - 维基百科,自由的百科全书

BFGS算法 维基百科,自由的百科全书 此條目或其章節极大或完全地依赖于某个 单一的来源 。 (2021年5月24日) 请协助補充多方面 可靠来源 以 改善这篇条目 。 在 数值 优化 中, Broyden–Fletcher–Goldfarb–Shanno(BFGS)算法 是一种求解无约束 非线性优化 问题的 迭代算法 。 和相关的Davidon–Fletcher–Powell算法类似,BFGS算法通过利用 曲率 信息对 梯度 进 …

Zh.wikipedia.org

DA: 16 PA: 28 MOZ Rank: 45

minimize_bfgs-API文档-PaddlePaddle深度学习平台

Minimize_bfgs¶. minimize_bfgs. 使用 BFGS 方法求解可微函数 objective_func 的最小值。. BFGS 是一种拟牛顿方法,用于解决可微函数上的无约束最优化问题。. 与之密切相关的是用于最优化的牛顿法,考虑迭代更新公式:. 如果 H k 是函数 f 在 x k 满足对称性和正定性,用来作为 ...

Paddlepaddle.org.cn

DA: 23 PA: 50 MOZ Rank: 48

一文读懂L-BFGS算法_winrar_setup.rar的博客-CSDN博 …

一、 L-BFGS是什么L-BFGS是解无约束非线性规划问题最常用的方法,具有收敛速度快、内存开销少等优点,在机器学习各类算法中常有它的身影。简单的说,L-BFGS和梯度下降、SGD干的同样的事情,但大多数情况下收敛 …

Blog.csdn.net

DA: 13 PA: 41 MOZ Rank: 57

LBFGS方法推导 - 慢慢的回味

BFGS方法推导. 可以构造一个二次函数来开始,假设 第k次迭代为:. 注意 为n x n对称正定矩阵,可以在每次迭代中更新。 注意到这个构造函数在p=0的时候, 和 和原函数是相同的。 对其求导,得到 . 公式2即为搜索方向向量,由此 第k+1次迭代为(其中 可以用强Wolfe准则来计 …

Liuxiaofei.com.cn

DA: 17 PA: 48 MOZ Rank: 69

L-BFGS 解优化问题 - pku.edu.cn

L-bfgs 解优化问题. 针对无约束优化问题. l-bfgs 在拟牛顿法 bfgs 迭代格式的基础上进行修改, 用以解决大规模问题的存储和计算困难。对于拟牛顿法中的迭代方向 考虑利用递归展开的方式进行求解。 首先,对于 bfgs 迭代格式, ,其中 。 将其递归地展开得到

Bicmr.pku.edu.cn

DA: 16 PA: 47 MOZ Rank: 68

优化算法——拟牛顿法之BFGS算法_TinyMind

一、bfgs算法简介 BFGS算法是使用较多的一种拟牛顿方法,是由Broyden,Fletcher,Goldfarb,Shanno四个人分别提出的,故称为BFGS校正。 同DFP校正的推导公式一样,DFP校正见博文“优化算法——拟牛顿法之DFP算法”。

Tinymind.net.cn

DA: 19 PA: 24 MOZ Rank: 49

SciPy中的经典优化算法 | msgsxj'blog

DFP方法与BFGS方法唯一的不同在于迭代更新Hessian阵本身而不是逆, 但由于仍然需要求逆操作, 因此计算量会比BFGS大上一圈. DFP方法的迭代公式可参考Numerical Optimization一书6.1节, 另一种的常见拟牛顿方法SR1可参考Numerical Optimization一书6.2节.

Msgsxj.cn

DA: 9 PA: 50 MOZ Rank: 43

基于L-BFGS反演算法的Δ T 精确计算磁异常分量 T ap 方法

笔者提出了基于有限储存BFGS(L-BFGS)反演算法的ΔT精确计算磁异常分量方法,首先推导了T ap 计算ΔT的正演公式,利用ΔT与T ap 的差值构建反演T ap 的目标函数,采用L-BFGS算法由ΔT解算T ap 。模型实验表明该方法计算得到的T ap 十分接近理论值,即可将误差降低两个数量级 ...

Wutanyuhuatan.com

DA: 21 PA: 28 MOZ Rank: 57

R optim() L-BFGS-B 需要 'fn' 的有限值 - Weibull - IT宝库

R optim() L-BFGS-B 需要 'fn' 的有限值 - Weibull问题的解决办法?R optim() L-BFGS-B 需要 'fn' 的有限值 - Weibull问题的解决方案?那么可以参考本文帮助大家快速定位并解决问题,译文如有不准确的地方,大家可以切到English参考源文内容。

Itbaoku.cn

DA: 14 PA: 50 MOZ Rank: 94

无约束优化方法(梯度法-牛顿法-BFGS- L-BFGS) - ooon - 博客园

本文讲解的是无约束优化中几个常见的基于梯度的方法,主要有梯度下降与牛顿方法、BFGS 与 L-BFGS 算法。. 梯度下降法是基于目标函数梯度的,算法的收敛速度是线性的,并且当问题是病态时或者问题规模较大时,收敛速度尤其慢(几乎不适用);. 牛顿法是 ...

Cnblogs.com

DA: 15 PA: 20 MOZ Rank: 45

Diagonal BFGS updates and applications to the limited memory …

We propose two diagonal BFGS-type updates. One is the diagonal part of the ordinary BFGS update on a diagonal matrix. The other is its inverse version. Both diagonal updates preserve the positive ...

Researchgate.net

DA: 20 PA: 50 MOZ Rank: 39

A GLOBALLY CONVERGENT BFGS METHOD FOR NONLINEAR …

BFGS method, monotone equation, hyperplane projection method, global convergence. This work was supported by the NSF (10771057 and 10701018) of China. c 2008 American Mathematical Society Reverts to public domain 28 years from publication 2231. 2232 WEI-JUN ZHOU AND DONG-HUI LI significant progress in the theoretical study on quasi-Newton methods, …

Maths.scnu.edu.cn

DA: 17 PA: 49 MOZ Rank: 78

BFGS(1) - Python实现 | 易学教程

BFGS作为一种拟牛顿法, 主要用于无法直接获取目标函数准确Hessian矩阵的情形. 此时退而求其次, 采用BFGS的更新方式获取目标函数的近似Hessian矩阵. 2. 需要注意的是, Hessian矩阵反映函数本身的曲率信息, 因此对函数响应的噪声部分非常敏感. 如果函数响应自带噪 …

E-learn.cn

DA: 14 PA: 14 MOZ Rank: 41

tfp.optimizer.bfgs_minimize | TensorFlow Probability

Usage: The following example demonstrates the BFGS optimizer attempting to find the minimum for a simple two dimensional quadratic objective function. minimum = np.array( [1.0, 1.0]) # The center of the quadratic bowl. scales = np.array( [2.0, 3.0]) # …

Tensorflow.org

DA: 18 PA: 50 MOZ Rank: 88

Limited-memory BFGS based least-squares pre-stack Kirchhoff …

The limited-memory BFGS (L-BFGS) method can evaluate the Hessian matrix indirectly using a limited amount of computer memory which only maintains a history of the past m gradients (often m < 10). We combine the L-BFGS method with least-squares pre-stack Kirchhoff depth migration. Then, we validate the introduced approach by the 2-D Marmousi synthetic data set and a 2-D …

Academic.oup.com

DA: 16 PA: 29 MOZ Rank: 60

tfp.optimizer.bfgs_minimize | TensorFlow Probability

Usage: The following example demonstrates the BFGS optimizer attempting to find the minimum for a simple two dimensional quadratic objective function. minimum = np.array( [1.0, 1.0]) # The center of the quadratic bowl. scales = np.array( [2.0, 3.0]) # The scales along the two axes.

Tensorflow.google.cn

DA: 20 PA: 50 MOZ Rank: 92

Spectral Scaling BFGS Method

E-mail: [email protected] D.H. Li ( ) School of Mathematical Sciences, South China Normal University, Guangzhou 510631, China e-mail: [email protected] . J Optim Theory Appl method may ...

Researchgate.net

DA: 20 PA: 50 MOZ Rank: 33

An Active Set Limited Memory BFGS Algorithm for Machine Learning

Guangxi University, Nanning 530004, China; [email protected] 2 School of Mathematics and Statistics, Baise University , Baise 533000, China 3 School of Business, Suzhou University of ...

Researchgate.net

DA: 20 PA: 50 MOZ Rank: 32

Low‐complexity linear massive MIMO detection based on the …

For lower complexity, BFGS method with the unit step size (named U-BFGS) is presented subsequently. When the base station (BS)-to-user-antenna ratio (BUAR) is large enough, the two proposed BFGS methods can be integrated (named U-S-BFGS) to further reduce complexity. In addition, an efficient initialization strategy is devised to accelerate …

Ietresearch.onlinelibrary.wiley.com

DA: 35 PA: 27 MOZ Rank: 81

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