Description: 一本介绍约束优化方面的经典书籍。对于从事约束优化算法的研究很有帮助。-A book introducing constrained programming. It is beneficial for the algorithm research of constrained programming Platform: |
Size: 11524096 |
Author:fd |
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Description: A new discrete fractional Fourier transform based on constrained eigendecomposition of DFT matrix by Lagrange multiplier method Platform: |
Size: 97280 |
Author:xiaowang |
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Description: RPCA (Robust Principal Component Analysis)是目前用于矩阵填充、图像去噪的最有效的优化方法。该代码是求解RPCA的一种数值算法——Exact ALM（Exact Augmented Lagrange Multiplier）-The most basic form of the exact ALM function is [A, E] = exact_alm_rpca(D, λ), and that of the inexact ALM function is [A, E] = inexact_alm_rpca(D, λ), where D is a real matrix and λ is a positive real number. We solve the RPCA problem using the method of augmented Lagrange multipliers. The method converges Q-linearly to the optimal solution. The exact ALM algorithm is simple to implement, each iteration involves computing a partial SVD of a matrix the size of D, and converges to the true solution in a small number of iterations. The algorithm can be further speeded up by using a fast continuation technique, thereby yielding the inexact ALM algorithm. Platform: |
Size: 380928 |
Author:Bingmiao Huang |
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Description: RPCA (Robust Principal Component Analysis)是目前用于矩阵填充、图像去噪的最有效的优化方法。目前最有效的算法是ALM（Augmented Lagrange Multiplier）。ALM分为Exact ALM和Inexact ALM。 该代码是Inexact ALM，收敛速度比Exact ALM快！-RPCA (Robust Principal Component Analysis) is used for matrix filling, image denoising. It is currently the most effective optimization method. Currently the most effective method is ALM (Augmented Lagrange Multiplier). There re 2 kinds of ALM: Exact ALM and Inexact ALM. The code is Inexact ALM, faster convergence speed than the Exact ALM! Platform: |
Size: 380928 |
Author:Bingmiao Huang |
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Description: How to Simulate A Ponytail - The Sample App
This is a very simple Lagrange Multiplier constrained dynamics
simulator to accompany my articles and lectures on How to Simulate a
Ponytail.
For more information, see http://chrishecker.com/How_to_Simulate_a_Ponytail
The code should be relatively portable. There s a glut interface that
has probably rotted but shouldn t be too hard to get working.
The Ax=b matrix solver is from http://www.netlib.org/c/sge.shar, and I
think it s public domain because it s a US Government contribution.
If you ve got Visual C++ 6.0 installed and have an OpenGL library, you can compile and execute it. -How to Simulate A Ponytail - The Sample App
This is a very simple Lagrange Multiplier constrained dynamics
simulator to accompany my articles and lectures on How to Simulate a
Ponytail.
For more information, see http://chrishecker.com/How_to_Simulate_a_Ponytail
The code should be relatively portable. There s a glut interface that
has probably rotted but shouldn t be too hard to get working.
The Ax=b matrix solver is from http://www.netlib.org/c/sge.shar, and I
think it s public domain because it s a US Government contribution.
If you ve got Visual C++ 6.0 installed and have an OpenGL library, you can compile and execute it. Platform: |
Size: 168960 |
Author:woojin |
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Description: 基本的拉格朗日乘子法(又称为拉格朗日乘数法)，就是求函数f(x1,x2,...)在g(x1,x2,...)=0的约束条件下的极值的方法。其主要思想是引入一个新的参数λ（即拉格朗日乘子），将约束条件函数与原函数联系到一起，使能配成与变量数量相等的等式方程，从而求出得到原函数极值的各个变量的解。
-Basic Lagrange multipliers (also known as Lagrange multiplier method), is of a function f (x1, x2 ,...) in g (x1, x2 ,...)= 0 constraints under the conditions of extreme value methods. The main idea is to introduce a new parameter λ (the Lagrange multiplier), the constraint function is linked together with the original function, so variables can be paired with an equal number of equations equation, thus obtained by the original function extreme solution of the various variables. Platform: |
Size: 1024 |
Author:马晓敏 |
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Description: 经典最优化方法最优化方法是一门古老而又年青的学科。这门学科的源头可以追溯到17世纪法国数学家拉格朗日关于一个函数在一组等式约束条件下的极值问题（求解多元函数极值的Lagrange乘数法）。-Classical optimization methods optimization method is an old and young subjects. The source of this discipline can be traced back to 17th century French mathematician Lagrange function in a group of about a condition of extreme equality constrained problem (solving the extreme value of the Lagrange multiplier Function method). Platform: |
Size: 233472 |
Author:张浩 |
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Description: 增广拉格朗日乘子方法求解RPCA问题的方法，得到矩阵的稀疏成分和低秩成分。-argumented lagrange multiplier method that can make matrix be decomposed to a sparse matrix and low-rank matrix. Platform: |
Size: 384000 |
Author:wangchangpeng |
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Search - lagrange multiplier