Algorithms.算法概论中文版.pdf

Complete Algorithms for Cooperative Pathfinding Problems

这只是解决问题的示例，大部分来自Leetcode，并提供使用Python的算法和数据结构。

在线聚类算法在数据科学中发挥着至关重要的作用，尤其是在时间、内存使用和复杂性方面的优势，同时与传统聚类方法相比保持了较高的性能。本教程服务于，首先，作为在线机器学习的调查，特别是数据流聚类方法。在本教程中，最先进的算法和相关的核心研究线程将通过识别不同的类别基于距离，密度网格和隐藏的统计模型。聚类有效性指标作为聚类过程中的一个重要组成部分，通常被忽略或被分类指标所取代，导致对最终结果的误解，也将被深入研究。 然后，本文将介绍River，一个由Creme和scikit-multiflow合并而成的go-to Python库。它也是第一个包含在线集群模块的开源项目，该模块可以促进可重复性，并允许直接进一步改进。在此基础上，我们提出了基于现实问题和数据集的聚类配置、应用程序和基准设置的方法。

Two-dimensional phase unwrapping: theory, algorithms, and software 书籍中的代码,很不错的东西，如果你会用到的话。

An overview of gradient descent optimization algorithms

This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensor-based planning, visibility, decision-theoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning., Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning, but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the ‘configuration spaces’ of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. This text and reference is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Inverse problems arise when we reconstruct a sharper image from a blurred one or reconstruct the underground mass density from measurements of the gravity above the ground. When we solve an inverse problem, we compute the source that gives rise to some observed data using a mathematical model for the relation between the source and the data. This book gives an introduction to the practical treatment of inverse problems by means of numerical methods, with a focus on basic mathematical and computational aspects. To solve inverse problems, we demonstrate that insight about them goes hand in hand with algorithms. Discrete Inverse Problems: Insight and Algorithms includes a number of tutorial exercises that give the reader hands-on experience with the methods, difficulties, and challenges associated with the treatment of inverse problems. It also includes examples and figures that illustrate the theory and algorithms. Audience This book is written for graduate students, researchers, and professionals in engineering and other areas that depend on solving inverse problems with noisy data. The aim is to provide readers with enough background that they can solve simple inverse problems and read more advanced literature on the subject. Contents Preface; List of Symbols; Chapter 1: Introduction and Motivation; Chapter 2: Meet the Fredholm Integral Equation of the First Kind; Chapter 3: Getting to Business: Discretizations of Linear Inverse Problems; Chapter 4: Computational Aspects: Regularization Methods; Chapter 5: Getting Serious: Choosing the Regularization Parameter; Chapter 6: Toward Real-World Problems: Iterative Regularization; Chapter 7: Regularization Methods at Work: Solving Real Problems; Chapter 8: Beyond the 2-Norm: The Use of Discrete Smoothing Norms; Appendix A: Linear Algebra Stuff; Appendix B: Symmetric Toeplitz-Plus-Hankel Matrices and the DCT; Appendix C: Early Work on 揟ikhonov Regularization? Bibliography; Index. About the Author Per Christian Hansen is Professor of Scientific Computing at the Technical University of Denmark. His publications include two other books on inverse problems, several MATLAB?packages, and many papers on inverse problems, matrix computations, and signal processing. His home page is http://www2.imm.dtu.dk/~pch/. To request an examination copy or desk copy of this book, please use our online request form at www.siam.org/catalog/adopt.php.

《Computer & Machine Vision: Theory Algorithms Practicalities》是一本非常经典的机器视觉教科书，其在Google Scholar上被应用超过2000次。 该资源是英文版PDF，含有目录。

Haxe2D矩形装箱算法。 运行演示。 基于的公共领域C ++ bin包装器。 特征 几种快速的近似装箱算法。 “占用率”用来比较包装性能。 可配置的包装试探法。 用法 在浏览器中运行，并参考。 基本用法示例： // Initialize a bin packer var binWidth : Int = 800 ; var binHeight : Int = 400 ; var useWasteMap : Bool = true ; var packer = new SkylinePacker ( binWidth , binHeight , useWasteMap ); // Start packing rectangles var rectWidth : Int = 20 ; var rectHeight : Int = 40 ; var heuristic : Leve