Convex vs nonconvex approaches for sparse estimation: Lasso, Multiple Kernel Learning and Hyperparameter Lasso 来自 arXiv.org 喜欢 0 阅读量: 86 作者:A Aravkin,JV Burke,A Chiuso,G Pillonetto 摘要: The popular Lasso approach for sparse estimation can be derived via marginalization of a joint ...
Convex functions on non-convex domains 来自 Semantic Scholar 喜欢 0 阅读量: 54 作者:HJM Peters,PP Wakker 摘要: It is shown that a convex function, defined on an arbitrary, possibly finite, subset of a linear space, can be extended to the whole space. An application to decision making ...
Convex and Nonconvex whubob EE WITH CS 搬砖 目录 收起 凸优化基础 凸优化基础2 非凸优化问题 对偶1 对偶2 优化技术进阶 常见只是点汇总 Nonconvex 凸优化基础 常见的凸优化问题 线性规划以及Simplex Method Stochastic LP P,NP,NPC问题 案列: 运输中的优化问题 打车中的优化问题 投放运营中的优化问题 ...
随笔分类 - 介绍凸优化与非凸优化的基本概念,常用算法,主要的研究成果及其在机器视觉,压缩感知,深度学习和信息论中的应用。 Big picture of mathematical optimization 摘要:Basic concepts, optimality conditions, different types of optimization, algorithm design techniques阅读全文 posted @2020-06-29 20:47科研民工...
大家或许不知道，（混合）整数规划被称为极度非凸问题（highly nonconvex problem），如下图：实心黑点...
In this paper, by considering the experts' fuzzy understanding of the nature of the parameters in the problem-formulation process, multiobjective nonconvex... Sakawa,Yauchi - 《IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics So...
网络非凸函数 网络释义 1. 非凸函数 非凸性规划,Non... ... ) nonconvex boundary 非凸边界 )nonconvex function非凸函数) nonconvex minimization 非凸极小 ... www.dictall.com|基于3个网页
The decomposition of the nonmonotone interface laws as the difference of monotone laws permits us to write a system of convex variational inequelities for the solution of the nonconvex elastostatic analysis problem under discussion. The nonconvex superpotential energy functional is written as a ...
In machine learning and optimization, one often wants to minimize a convex objective function $F$ but can only evaluate a noisy approximation $\hat{F}$ to it. Even though $F$ is convex, the noise may render $\hat{F}$ nonconvex, making the task of minimizing $F$ intractable in general...
这个专题我们专注于lossless convexification and successive Convexification. 本文基于scvx,介绍了两个最基本的凸化方法,保证这个序列凸化的稳定性,分别是virtual control,以及trust regions, 都是非常经典的方案了。并且作者还给出了收敛性分析。这里就只记录这两个方法,收敛性证明部分直接跳过不看了,有兴趣可以过一遍。