^Successive Convex Approximation: Analysis and Applicationshttps://conservancy.umn.edu/handle/11299/163884 ^Regularized Robust Estimation of Mean and Covariance Matrix Under Heavy-Tailed Distributionshttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7069228...
SCA，即连续性凸逼近方法，是一种通过不断迭代解决非凸优化问题的策略。术语"successive"表示连续性和迭代过程，意味着SCA通过逐步改善解来逐步逼近最优解。术语"convex"指代在迭代过程中使用凸函数来替换非凸函数。凸函数的特性使得优化问题在每一步迭代中变得更为简单，便于求解。"approximation"意味着使用...
convex: 就是说在迭代的过程中采用的是凸函数来代替非凸函数。approximation: 怎么去采用凸函数来代替非...
Successive Convex Approximation(连续凸近似,SCA)是一种求解非凸优化问题的处理方法,它将非凸优化问题转化为一系列凸问题,从而得到原问题的近似解。1. 非凸优化问题描述2. SCA求解非凸优化问题求解非凸问题(1)已经转化为求解凸优化问题(5),然后应用凸优化方法[2]进行求解即可。
SCA（Successive Convex Approximation）在工程优化领域是一个强大的工具，尤其是在处理非凸优化问题时。本文将详细介绍SCA的基本原理、算法、收敛性以及实际应用，与主流的优化方法MM（Majorization-Minimization）进行对比，并提供实例说明其使用方法。SCA原理**：SCA与MM共享相似的基本思路，都是通过在某个点...
In this paper, we propose a successive convex approximation framework for sparse optimization where the nonsmooth regularization function in the objective function is nonconvex and it can be written as the difference of two convex functions. The proposed framework is based on a nontrivial combination...
Our analysis unifies and extends the existing convergence results for many classical algorithms such as the BCD method, the difference of convex functions (DC) method, the expectation maximization (EM) algorithm, as well as the classical stochastic (sub-)gradient (SG) method for the nonsmooth ...
逐次凸近似(Successive Convex Approximation, SCA)是一种优化算法，主要应用于解决非凸优化问题。其基本思路是将一个非凸问题转化为包含多个凸子问题的序列，通过连续求解凸子问题来逼近原问题的最优解。现以如下非凸二次规划问题为例，其函数图像如图1所示。其中，原问题的目标函数可以通过特征值分解转换...
网络连续凸近似 网络释义 1. 连续凸近似 因此我们提出了一个连续凸近似(successive convex approximation)方法来快速得到一 波束成型之近似解。我们更证明了由我们 … ir.lib.nthu.edu.tw|基于2个网页
Finally, the long-term variables are updated by solving a convex approximation problem obtained by replacing the objective function in the long-term master problem with the convex surrogate function. We establish the almost sure convergence of the TOSCA algorithm and customize the algorithmic framework...