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

Hazan, Elad

  • 出版商: Summit Valley Press
  • 出版日期: 2022-09-06
  • 售價: $1,480
  • 貴賓價: 9.8$1,450
  • 語言: 英文
  • 頁數: 248
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 0262046989
  • ISBN-13: 9780262046985
  • 立即出貨 (庫存=1)



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





- 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。