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

海豹 ⠀ ⠀⠀ 半监督图分类的PyTorch实现：分层图透视（WWW 2019） 抽象的 节点分类和图分类是两个图学习问题，它们分别预测节点的类标签和图的类标签。 图的节点通常代表现实世界的实体，例如，社交网络中的用户或蛋白质-蛋白质相互作用网络中的蛋白质。 在这项工作中，我们考虑一个更具挑战性但实际上有用的设置，其中节点本身是一个图实例。 这导致了分层图的透视图，这种透视图出现在许多领域中，例如社交网络，生物网络和文档收集。 例如，在社交网络中，一群具有共同兴趣的人形成一个用户组，而许多用户组则通过交互或普通成员相互连接。 我们在层次图中研究节点分类问题，其中“节点”是图实例，例如上述示例中的用户组。 由于标签通常受限于实际数据，因此我们通过谨慎/主动迭代（或简称SEAL-C / AI）设计了两种新颖的半监督解决方案，称为半监督图分类。 SEAL-C / AI采用了一个迭代框架，该框

Algorithms to Live by:The Computer Science of Human Decisions中文名：算法之美 《算法之美》是2018年5月由中信出版集团出版的一本图书，作者是布莱恩·克里斯汀和汤姆·格里菲思。本书通过讨论人类事务算法设计的概念，以帮助人们更好地处理日常生活中遇到的难题。万维钢、查尔斯·都希格等人对本书做出了评价。

Data Structures and Algorithms Using Python and C++ 数据结构与算法方面的书籍

Two Dimensional Phase Unwrapping: Theory, Algorithms and Software by Dennis C. Ghiglia & Mark D. Pritt Part 2 of 4