Introduction to Online Convex Optimization, 2/e (Hardcover)

New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process.

In many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and/or mathematical optimization. Introduction to Online Convex Optimization presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed. This view of optimization as a process has led to some spectacular successes in modeling and systems that have become part of our daily lives.

Based on the "Theoretical Machine Learning" course taught by the author at Princeton University, the second edition of this widely used graduate level text features:

• Thoroughly updated material throughout
• New chapters on boosting, adaptive regret, and approachability and expanded exposition on optimization
• Examples of applications, including prediction from expert advice, portfolio selection, matrix completion and recommendation systems, SVM training, offered throughout
• Exercises that guide students in completing parts of proofs

這是一本研究線上凸優化的研究生級教科書的新版本，該教科書將優化視為一個過程的機器學習框架。

在許多實際應用中，環境非常複雜，無法建立全面的理論模型並使用傳統的算法理論和/或數學優化。《線上凸優化入門》提出了一種強大的機器學習方法，其中包含數學優化、博弈論和學習理論的元素：一種從經驗中學習的優化方法，隨著問題的不斷觀察到更多方面，進行調整。這種將優化視為一個過程的觀點已經在建模和系統方面取得了一些驚人的成功，並成為我們日常生活的一部分。

基於作者在普林斯頓大學教授的《理論機器學習》課程，這本廣泛使用的研究生級教科書的第二版具有以下特點：

- 全書內容全面更新
- 新增了有關增強、自適懊悔和可接近性的章節，並對優化進行了擴展說明
- 整本書都提供了應用示例，包括從專家建議中進行預測、投資組合選擇、矩陣填充和推薦系統、支持向量機訓練等
- 練習題引導學生完成部分證明的過程

Elad Hazan is Professor of Computer Science at Princeton University and cofounder and director of Google AI Princeton. An innovator in the design and analysis of algorithms for basic problems in machine learning and optimization, he is coinventor of the AdaGrad optimization algorithm for deep learning, the first adaptive gradient method.

Elad Hazan是普林斯頓大學的計算機科學教授，也是Google AI Princeton的聯合創始人和主管。他在機器學習和優化的基本問題的算法設計和分析方面是一位創新者，他是深度學習中AdaGrad優化算法的共同發明人，這是第一個自適應梯度方法。