Robust Optimization

第二版全面升級成全彩印刷，閱讀上更加美觀舒適！ 內容也更新成最新技術，快速適應未來潮流！ 「前端效能優化」最全面的書籍！ 精通前端基礎和優化技術，為你打造高效能網站！ 本書內容改編自第 13 屆 2021 iThome 鐵人賽，Modern Web 組冠軍網路系列文章──《今晚，我想來點 Web 前端效能優化大補帖！》。本書彙整了網頁前端應用效能優化的各種技巧，並以此為出發點，延伸至許多前端領域必備的知識。搭配簡易圖文和範例檔實作，讓你打造高效能的前端應用，解決網站效能痛點，提升速度與使用者體驗，增加網站曝光率與流量！ 本次改版除了將一些過時的內容更新，以及新增新的章節以外，也驗證了大部分的篇章概念是不會因為前端技術本身或是 AI 的發展而變成無用的知識。作者希望透過「前端效能優化」這個主題協助讀者掌握前端開發裡的「底層邏輯」，在快速變化的前端領域掌握不變的核心概念，培養能夠快速適應未來改變的扎實基底！ 專注底層邏輯才能永不過時！ 讓你增進網站效能的四劑大補帖 ▍小細節讓效能UP 除了依賴指標，還要從對的地方著手！ ▍前端開發心法 用對優化工具和技術，提升效能&使用者體驗。 ▍深入技術原理 介紹前端技術原理，精通前端應用知識。 ▍提供完整範例 跟著實作範例學習，強化前端優化技能！ 【精彩內容】 •認識 Core Web Vitals、RAIL Model、Lighthouse 等指標和效能監測工具，找出效能不足的地方。 •建立前端必備知識：瀏覽器架構與渲染流程、網路與快取、JavaScript 記憶體管理機制，並學習正確的圖片資源、檔案壓縮與打包技術。 •在不同情境下使用正確的優化技術：Code Splitting、動態載入、Tree Shaking、模組化技巧、Web Workers 與 WebAssembly。 •使用 DevTool 檢測網站效能、實作 Debounce 與 Throttle，達到網站節流。 【目標讀者】 ✦想要了解各種效能優化技巧的前端開發者 ✦想要更理解前端開發底層知識的開發者 ✦想了解前端開發近期發展與未來趨勢的讀者 下載範例程式檔案 本書的程式碼是由GitHub託管，可點選下面圖案前往下載： https://github.com/kylemocode/f2e-performance-optimization-book-demo 升級全彩

在人們的生活中，會遇到各式各樣的優化問題。如何快速高效地處理優化問題也成了當今優化領域內研究的重點。生物地理學優化(Biogeography-based optimization, BBO)算法是群智能優化算法之一，由美國學者Simon于2008年提出。經過10年的BBO相關研究雖然一定程度上改進了算法，並能夠很好地處理一些優化問題，但對於隨著社會的發展，面臨優化問題的複雜性和多樣性也在不斷提升，出現了更多更為複雜的多峰、非線性不可分優化問題，這些問題對優化方法的性能和效率發起了巨大挑戰。因此，BBO依然有著改進空間和研究潛力。目前國內出版的BBO相關研究專著並不多，已發表的BBO綜述性論文數量也很有限，且受版面限制等因素，這些綜述對BBO的介紹及原理解釋都不夠詳盡。本書以優化問題開篇，逐漸引入群智能優化算法的概念，由群智能優化算法逐步引入BBO，對BBO背景、原理、存在的缺陷及改進動機進行了詳細介紹，對BBO目前國內外研究現狀進行了綜述，對BBO各步驟代表性改進研究進行了簡述，並詳細描述了6項作者課題組對BBO的創新性改進研究。6項研究分別為"差分遷移和趨優變異的BBO算法(DGBBO)”、"差分變異和交叉遷移的BBO算法(DCBBO)”、"混合交叉的BBO算法(HCBBO)”、"高效融合的BBO算法(EMBBO)”、"GWO與BBO的混合算法(HBBOG)”和"SFLA與BBO的混合算法(HBBOS)”。在本書第4至9章內容中，描述了這些算法的原理，並通過大量基準函數實驗對比了當前最先進的算法，驗證對BBO的改進效果。在本書第4至9章內容中，解釋了這些算法的原理，並通過大量實驗對比了當前最先進的算法，驗證對BBO的改進效果。6種新算法均作為BBO改進算法，在邏輯關係上是並列且相互獨立的。最後在10-12章描改進的BBO在圖像分割上的應用。

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New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process.In many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and/or mathematical optimization. Introduction to Online Convex Optimization presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed. This view of optimization as a process has led to some spectacular successes in modeling and systems that have become part of our daily lives. Based on the “Theoretical Machine Learning” course taught by the author at Princeton University, the second edition of this widely used graduate level text features:Thoroughly updated material throughoutNew chapters on boosting, ad

This book describes a comprehensive framework for hardware/software co-design, optimization, and use of robust, low-cost, and cyberphysical digital microfluidic systems. Readers with a background in e

Over the past two decades there have been significant advances in the field of optimization. In particular, convex optimization has emerged as a powerful signal processing tool, and the variety of applications continues to grow rapidly. This book, written by a team of leading experts, sets out the theoretical underpinnings of the subject and provides tutorials on a wide range of convex optimization applications. Emphasis throughout is on cutting-edge research and on formulating problems in convex form, making this an ideal textbook for advanced graduate courses and a useful self-study guide. Topics covered range from automatic code generation, graphical models, and gradient-based algorithms for signal recovery, to semidefinite programming (SDP) relaxation and radar waveform design via SDP. It also includes blind source separation for image processing, robust broadband beamforming, distributed multi-agent optimization for networked systems, cognitive radio systems via game theory, and t

Optimization problems arising in practice usually contain several random parameters. Hence, in order to obtain optimal solutions being robust with respect to random parameter variations, the mostly av

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Differential evolution is a very simple but very powerful stochastic optimizer. Since its inception, it has proved very efficient and robust in function optimization and has been applied to solve prob

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Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; found

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Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically. This introduction to optimization attempts to strike

This book is about optimization techniques and is subdivided into two parts. In the first part a wide overview on optimization theory is presented. Optimization is presented as being composed of five

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The volume presents a collaboration between internationally recognized experts on anti-optimization and structural optimization, and summarizes various novel ideas, methodologies and results studied

Eschewing a more theoretical approach, Portfolio Optimization shows how the mathematical tools of linear algebra and optimization can quickly and clearly formulate important ideas on the subject. This

Structural Optimization is intended to supplement the engineer's box of analysis and design tools making optimization as commonplace as the finite element method in the engineering workplace. It begi

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This self-contained textbook is an informal introduction to optimization through the use of numerous illustrations and applications. The focus is on analytically solving optimization problems with a f

Aspects of Robust Statistics are important in many areas. Based on the International Conference on Robust Statistics 2001 (ICORS 2001) in Vorau, Austria, this volume discusses future directions of the

An introduction to the theory and methods of robust statistics, providing students with practical methods for carrying out robust procedures in a variety of statistical contexts and explaining the adv

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A unique text integrating numerics, mathematics and applications to provide a hands-on approach to using optimization techniques, this mathematically accessible textbook emphasises conceptual understanding and importance of theorems rather than elaborate proofs. It allows students to develop fundamental optimization methods before delving into MATLAB®'s optimization toolbox, and to link MATLAB's results with the results from their own code. Following a practical approach, the text demonstrates several applications, from error-free analytic examples to truss (size) optimization, and 2D and 3D shape optimization, where numerical errors are inevitable. The principle of minimum potential energy is discussed to highlight the deep relationship between engineering and optimization. MATLAB code in every chapter illustrates key concepts and the text demonstrates the coupling between MATLAB and SOLIDWORKS® for design optimization. A wide variety of optimization problems are covered including con

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Optimization methods play a central role in financial modeling. This textbook is devoted to explaining how state-of-the-art optimization theory, algorithms, and software can be used to efficiently solve problems in computational finance. It discusses some classical mean–variance portfolio optimization models as well as more modern developments such as models for optimal trade execution and dynamic portfolio allocation with transaction costs and taxes. Chapters discussing the theory and efficient solution methods for the main classes of optimization problems alternate with chapters discussing their use in the modeling and solution of central problems in mathematical finance. This book will be interesting and useful for students, academics, and practitioners with a background in mathematics, operations research, or financial engineering. The second edition includes new examples and exercises as well as a more detailed discussion of mean–variance optimization, multi-period models, and add

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This book presents an introduction to optimization, offering the reader the fundamentals of several methods with accompanying practical engineering applications. It includes optimization calculations