Algorithm 算法代写

算法代写代考包括以下内容:

  1. 概念:时间复杂度、空间复杂度、算法分析、数据结构代写
  2. 搜索算法:二叉搜索树、哈希表
  3. 排序算法:快速排序、归并排序
  4. 动态规划算法代写
  5. 图论:最短路径算法代写
  6. 数学:数论代写
  7. 代码实现:C / C++ / Java / Python代写

Algorithms courses typically include topics such as data structures, basic algorithms, graph algorithms, dynamic programming, computational geometry, and number theory. They can also include more advanced topics such as parallel algorithms, randomized algorithms, and approximation algorithms.

CSC373 F24 作业1

截止日期:9月30日,午夜 指南:(请完整阅读!) 问题1.(11分) 在这个问题中,你将得到实线上的n个区间I1 = [a1, b1],…,In = [an, bn]作为输入。每个区间$I_{j}$由两个数$a_{j}$和$b_{j}$指定。我们假设对于所有j,$a_{j} < b_{j}$。我们还假设没有两个区间共享它们的任何端点,即所有的数$a_{1},…,a_{n},b_{1},…,b_{n}$都是不同的。这些区间在两个数组A[1…n]和B[1…n]中给出,其中$A[j] = a_{j}$,$B[j] = b_{j}$。 我们说区间$I_{j}$和$I_{k}$交叉,如果$I_{j} \cap I_{k} ≠ \emptyset$,但两个区间都不包含另一个。换句话说,如果$I_{k}$的恰好一个端点包含在$I_{j}$中,那么$I_{j}$和$I_{k}$交叉。 部分a.(3分)假设存在某个数x,使得对于所有j,$a_{j} < x$,并且对于所有j,$b_{j} > x$。给出一个在最坏情况下时间复杂度为$O(n log n)$的算法来计算满足$j < k$且$I_{j}$和$I_{k}$交叉的对数。证明你的答案。 提示:使用课堂上的一种分治算法。 部分b.(8分)给出一个分治算法来计算满足$j < k$且$I_{j}$和$I_{k}$交叉的对数,而不使用前一个子问题中的假设。你的算法应在最坏情况下的时间复杂度为$O(n log ^{2} n)$。证明你的答案。 部分c.(7分)(加分问题 – 可选)。修改你的算法,使其在最坏情况下的时间复杂度为$O(n log n)$。证明你的答案。 提示:尝试仅调用一次时间复杂度为$O(n log n)$的任何子例程,而不是递归调用。 问题2.(19分) 在这个问题中,你需要为k家杂货店确定位置,以服务于街道上的n所房屋。假设我们将街道建模为从0到1的区间,并且房屋的位置由实数$x_{1},…,x_{n} \in [0,1]$给出。你可以假设$x_{1} < x_{2}… < x_{n}$,即位置是从左到右排序的,并且它们以数组$x[1…n]$的形式提供给你,其中$x[i] = x_{i}$。每周,每个房屋中的一个人会前往最近的杂货店购买食品杂货。你的目标是计算$y_{1},…,y_{k} […]

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STAT 861: Theoretical Foundations of Machine Learning University of Wisconsin–Ma

CS/ECE/STAT-861: Theoretical Foundations of Machine Learning University of Wisconsin–Madison, Fall 2023 Homework 2. Due 10/27/2023, 11.00 am Instructions: 1. Homework is due at 11 am on the due date. Please hand over your homework at the beginning of class. Please see the course website for the policy on late submission. 2. I recommend that you

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STAT 861: Theoretical Foundations of Machine Learning University of Wisconsin–Ma

CS/ECE/STAT-861: Theoretical Foundations of Machine Learning University of Wisconsin–Madison, Fall 2023 Homework 0. Due 9/15/2023, 11.00 am Instructions: 1. Homework is due at 11 am on the due date. Please hand over your homework at the beginning of class. Please see the course website for the policy on late submission. 2. I recommend that you

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STAT 861: Theoretical Foundations of Machine Learning University of Wisconsin–Ma

CS/ECE/STAT-861: Theoretical Foundations of Machine Learning University of Wisconsin–Madison, Fall 2023 Homework 3. Due 11/08/2023, 11.00 am Instructions: 1. Homework is due at 11 am on the due date. Please hand over your homework at the beginning of class. Please see the course website for the policy on late submissions. 2. I recommend that you

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CS861: Theoretical Foundations of Machine Learning Lecture 22 10 25 2023 Unive

CS861: Theoretical Foundations of Machine Learning Lecture 22 – 10/25/2023 University of Wisconsin–Madison, Fall 2023 Lecture 22: Online learning, The experts problem Lecturer: Kirthevasan Kandasamy Scribed by: Xinyan Wang, Zhifeng Chen Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only with

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CS861: Theoretical Foundations of Machine Learning Lecture 1 10 20 2023 Univer

CS861: Theoretical Foundations of Machine Learning Lecture 1 – 10/20/2023 University of Wisconsin–Madison, Fall 2023 Lecture 20: Structured Bandits, Martingales Lecturer: Kirthevasan Kandasamy Scribed by: Alex Clinton, Chenghui Zheng Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only with the permission

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1912.13213v6

A Modern Introduction to Online Learning Francesco Orabona Boston University May 30, 2023 arXiv:1912.13213v6 [cs.LG] 28 May 2023 Abstract vi 1 What is Online Learning? 1 1.1 HistoryBits………………………………………… 5 2 Online Subgradient Descent 7 2.1 OnlineLearningwithConvexDifferentiableLosses……………………… 7 2.1.1 ConvexAnalysisBits:Convexity ………………………….. 8 2.1.2 OnlineGradientDescent………………………………. 10 2.2 OnlineSubgradientDescent ………………………………… 12 2.2.1 ConvexAnalysisBits:Subgradients…………………………. 13 2.2.2 AnalysiswithSubgradients …………………………….. 14

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