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随机凸程序的边缘相变生存

Living on the Edge - Phase Transitions in Random Convex Programs
课程网址: http://videolectures.net/roks2013_tropp_optimization/  
主讲教师: Joel Tropp
开课单位: 加州理工学院
开课时间: 2013-08-26
课程语种: 英语
中文简介:
最近的实证研究表明,随着约束数量的增加,许多具有随机约束的凸优化问题呈现出相变。例如,这种现象出现在用于从随机线性样本中识别稀疏向量的l1最小化方法中。事实上,当样本数量超过取决于稀疏程度的阈值时,这种方法很有可能成功;否则,它很可能失败。 这篇演讲总结了一个严谨的分析,解释了为什么相变在随机凸优化问题中无处不在。它还描述了用于对过渡的定量方面进行可靠预测的工具,包括过渡区域的位置和宽度。这些技术适用于具有随机测量的正则化线性逆问题,适用于随机非相干模型下的分层问题,也适用于带有随机仿射约束的圆锥程序。与D.Amelunxen、M.Lotz和M.B.McCoy合作
课程简介: Recent empirical research indicates that many convex optimization problems with random constraints exhibit a phase transition as the number of constraints increases. For example, this phenomenon emerges in the l1 minimization method for identifying a sparse vector from random linear samples. Indeed, this approach succeeds with high probability when the number of samples exceeds a threshold that depends on the sparsity level; otherwise, it fails with high probability. This talk summarizes a rigorous analysis that explains why phase transitions are ubiquitous in random convex optimization problems. It also describes tools for making reliable predictions about the quantitative aspects of the transition, including the location and the width of the transition region. These techniques apply to regularized linear inverse problems with random measurements, to demixing problems under a random incoherence model, and also to cone programs with random affine constraints. Joint work with D. Amelunxen, M. Lotz, and M. B. McCoy
关 键 词: 约束数量; 凸优化问题; 随机仿射
课程来源: 视频讲座网
数据采集: 2022-11-29:chenjy
最后编审: 2022-11-29:chenjy
阅读次数: 24