算法与数据结构 ( Algorithms and data structures )包括算法分析( Analysis of algorithms ),算法( Algorithms ),数据结构( Data structures )以及计算几何( Computational geometry ) Golden Formula: Algorithms + Data Structures = Programs

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这本教科书解释的概念和技术需要编写的程序,可以有效地处理大量的数据。面向项目和课堂测试,这本书提出了一些重要的算法,由例子支持,给计算机程序员面临的问题带来意义。计算复杂性的概念也被介绍,演示什么可以和不可以被有效地计算,以便程序员可以对他们使用的算法做出明智的判断。特点:包括介绍性和高级数据结构和算法的主题,与序言顺序为那些各自的课程在前言中提供; 提供每个章节的学习目标、复习问题和编程练习,以及大量的说明性例子; 在相关网站上提供可下载的程序和补充文件,以及作者提供的讲师资料; 为那些来自不同的语言背景的人呈现Python的初级读本。

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"Bandits with Knapsacks" (BwK) is a general model for multi-armed bandits under supply/budget constraints. While worst-case regret bounds for BwK are well-understood, we present three results that go beyond the worst-case perspective. First, we provide upper and lower bounds which amount to a full characterization for logarithmic, instance-dependent regret rates. Second, we consider "simple regret" in BwK, which tracks the algorithm's performance in a given round, and prove that it is small in all but a few rounds. Third, we provide a general template for extensions from bandits to BwK which takes advantage of some known helpful structure and apply this template to combinatorial semi-bandits and linear contextual bandits. Our results build on the BwK algorithm from (Agrawal and Devanur, 2014), providing new analyses thereof.

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"Bandits with Knapsacks" (BwK) is a general model for multi-armed bandits under supply/budget constraints. While worst-case regret bounds for BwK are well-understood, we present three results that go beyond the worst-case perspective. First, we provide upper and lower bounds which amount to a full characterization for logarithmic, instance-dependent regret rates. Second, we consider "simple regret" in BwK, which tracks the algorithm's performance in a given round, and prove that it is small in all but a few rounds. Third, we provide a general template for extensions from bandits to BwK which takes advantage of some known helpful structure and apply this template to combinatorial semi-bandits and linear contextual bandits. Our results build on the BwK algorithm from (Agrawal and Devanur, 2014), providing new analyses thereof.

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