这本书是由 Michael Nielsen 和 Isaac Schreiber 合著的，于2019年由MIT出版社出版。该书是机器学习领域中关于非凸优化问题的经典著作之一，主要介绍了一些非凸优化算法以及如何求解非凸优化问题。书中主要讲了两种非凸情况：一是目标函数是凸的，约束集合不是凸的，即 f(x) 凸，C 非凸；二是目标函数不是凸的...
Artificial intelligence Convex Optimization Algorithms and Recovery Theories for Sparse Models in Machine Learning COLUMBIA UNIVERSITY Donald Goldfarb HuangBoSparse modeling is a rapidly developing topic that arises frequently in areas such as machine learning, data analysis and signal processing. One ...
Non-convex Optimization for Machine Learning takes an in... (展开全部) 目录· ··· Table of contents: Preface Mathematical Notation Part I: Introduction and Basic Tools 1. Introduction 2. Mathematical Tools ··· (更多) 丛书信息·
Parameter Learning: a Convex Analytic Path Abstract The goal of this chapter is to present an overview of techniques forconvex optimizationin the context of machine learning. It starts from the definitions ofconvex sets, functions, and the projection operator and some of its properties are derived...
Numerical Optimization 9.2 Introductory Lectures on Convex O... 9.4 The Elements of Statistical Learnin... 9.4 Optimization for Machine Learning 9.4 Statistical Inference 9.2 Matrix Algebra High-Dimensional Probability 9.7 Asymptotic Statistics 9.1 Matrix Analysis (Graduate Texts in ... ...
Online convex optimization is a sequential decision-making problem with a sequence of arbitrarily varying convex loss functions. This problem has gained renewed interest since it is a promising framework for machine learning and has wide applications. This chapter provides an overview of online convex ...
Pattern Recognition and Machine L...9.5 Nonlinear Programming9.0 Learning with Kernels9.8 Predicting Structured Data Optimization for Machine Learning9.4 Machine Learning9.1 Foundations of Machine Learning8.9 Graphical Models, Exponential Fa...9.7
Non-convexoptimizationisubiquitousinmodernmachinelearning:recentbreak- throughsindeeplearningrequireoptimizingnon-convextrainingobjectivefunctions; problemsthatadmitaccurateconvexrelaxationcanoftenbesolvedmoreefficiently withnon-convexformulations.However,thetheoreticalunderstandingofnon-convex ...
USC: ISE 633: Large Scale Optimization for Machine Learning, 2019 Fall
convex optimization, using cutting plane/ellipsoid/subgradientconvex optimization to machine learning, and significanceoptimization problems, linear/geometric programming/Lagrange Dualitydeterministic/stochastic algorithms in solving optimization problemsrobust optimization, for robust waveform diversity...