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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This paper considers a variant of the online paging problem, where the online algorithm has access to multiple predictors, each producing a sequence of predictions for the page arrival times. The predictors may have occasional prediction errors and it is assumed that at least one of them makes a sublinear number of prediction errors in total. Our main result states that this assumption suffices for the design of a randomized online algorithm whose time-average regret with respect to the optimal offline algorithm tends to zero as the time tends to infinity. This holds (with different regret bounds) for both the full information access model, where in each round, the online algorithm gets the predictions of all predictors, and the bandit access model, where in each round, the online algorithm queries a single predictor. While online algorithms that exploit inaccurate predictions have been a topic of growing interest in the last few years, to the best of our knowledge, this is the first paper that studies this topic in the context of multiple predictors. Moreover, to the best of our knowledge, this is also the first paper that aims for (and achieves) online algorithms with a vanishing regret for a classic online problem under reasonable assumptions.

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This paper considers a variant of the online paging problem, where the online algorithm has access to multiple predictors, each producing a sequence of predictions for the page arrival times. The predictors may have occasional prediction errors and it is assumed that at least one of them makes a sublinear number of prediction errors in total. Our main result states that this assumption suffices for the design of a randomized online algorithm whose time-average regret with respect to the optimal offline algorithm tends to zero as the time tends to infinity. This holds (with different regret bounds) for both the full information access model, where in each round, the online algorithm gets the predictions of all predictors, and the bandit access model, where in each round, the online algorithm queries a single predictor. While online algorithms that exploit inaccurate predictions have been a topic of growing interest in the last few years, to the best of our knowledge, this is the first paper that studies this topic in the context of multiple predictors. Moreover, to the best of our knowledge, this is also the first paper that aims for (and achieves) online algorithms with a vanishing regret for a classic online problem under reasonable assumptions.

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