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Algorithm Engineering for Integral and Dynamic Problems
by Lucia RapanottiAlgorithm engineering allows computer engineers to produce a computational machine that will execute an algorithm as efficiently and cost-effectively as possible given a set of constraints, such as minimal performance or the availability of technology. Addressing algorithm engineering in a parallel setting, regular array syntheses offer powerful co
Algorithm Portfolios: Advances, Applications, and Challenges (SpringerBriefs in Optimization)
by Dimitris Souravlias Konstantinos E. Parsopoulos Ilias S. Kotsireas Panos M. PardalosThis book covers algorithm portfolios, multi-method schemes that harness optimization algorithms into a joint framework to solve optimization problems. It is expected to be a primary reference point for researchers and doctoral students in relevant domains that seek a quick exposure to the field. The presentation focuses primarily on the applicability of the methods and the non-expert reader will find this book useful for starting designing and implementing algorithm portfolios. The book familiarizes the reader with algorithm portfolios through current advances, applications, and open problems. Fundamental issues in building effective and efficient algorithm portfolios such as selection of constituent algorithms, allocation of computational resources, interaction between algorithms and parallelism vs. sequential implementations are discussed. Several new applications are analyzed and insights on the underlying algorithmic designs are provided. Future directions, new challenges, and open problems in the design of algorithm portfolios and applications are explored to further motivate research in this field.
Algorithmen in der Graphentheorie: Ein konstruktiver Einstieg in die Diskrete Mathematik (essentials)
by Katja Mönius Jörn Steuding Pascal StumpfDieses essential liefert eine Einführung in die Graphentheorie mit Fokus auf ihre algorithmischen Aspekte; Vorkenntnisse werden dabei nicht benötigt. Ein Graph ist ein Gebilde bestehend aus Ecken und verbindenden Kanten. Wir untersuchen Kreise in Graphen, wie sie etwa beim Problem der Handlungsreisenden oder des chinesischen Postboten auftreten, fragen uns, wie sich mithilfe von Graphen (und insbesondere Bäumen) Routen planen lassen, und machen uns an die Färbung von Graphen, wobei keine benachbarten Ecken mit derselben Farbe versehen werden sollen. Diese klassischen Themen der Graphentheorie werden durch eine Vielzahl von Illustrationen und Algorithmen untermalt, über deren Laufzeit wir uns ebenfalls Gedanken machen. Viele bunte Beispiele erleichtern den Einstieg in dieses aktuelle und vielseitige Gebiet der Mathematik.
Algorithmen, Zufall, Unsicherheit – und Pizza!: Wie Mathematik uns hilft, alltägliche Entscheidungen zu treffen
by Florian HeinrichsDieses Buch lädt dazu ein, die Welt um uns herum aus einem neuen Blickwinkel zu betrachten und dabei die spannende Verbindung zwischen der Mathematik und unserem täglichen Leben zu entdecken – denn um die Technologien und Entwicklungen unserer modernen Gesellschaft zu verstehen, benötigen wir ein intuitives Verständnis grundlegender mathematischer Ideen. In diesem Buch geht es um diese Grundlagen, vor allem aber um ihre praktische Anwendung im Alltag: Gemeinsam begeben wir uns auf eine unterhaltsame Reise und entdecken dabei, wie Mathematik in vielfältiger Weise allgegenwärtig ist. Anschauliche Beispiele zeigen, wie wir täglich – oft unbewusst – mathematische Ideen nutzen und wie wir mit Hilfe von Mathematik bessere Entscheidungen treffen können. Nach einer Einführung in Algorithmen und Optimierungsprobleme, geht es im weiteren Verlauf um die Modellierung von Zufall und Unsicherheiten. Zum Ende des Buchs werden die Themen zusammengeführt und Algorithmen für Anwendungen besprochen, bei denen der Zufall eine entscheidende Rolle spielt. Sie erfahren unter anderem: Wie Sie systematisch Ordnung in Ihre Plattensammlung bringen können Warum ein vermeintliches Optimum nicht immer optimal ist Wie viele Getränke Sie bei einer Party bereitstellen sollten, damit niemand durstig nach Hause geht Wann Sie besser den nächsten freien Parkplatz nehmen sollten
Algorithmic Advances in Riemannian Geometry and Applications: For Machine Learning, Computer Vision, Statistics, and Optimization (Advances in Computer Vision and Pattern Recognition)
by Hà Quang Minh Vittorio MurinoThis book presents a selection of the most recent algorithmic advances in Riemannian geometry in the context of machine learning, statistics, optimization, computer vision, and related fields. The unifying theme of the different chapters in the book is the exploitation of the geometry of data using the mathematical machinery of Riemannian geometry. As demonstrated by all the chapters in the book, when the data is intrinsically non-Euclidean, the utilization of this geometrical information can lead to better algorithms that can capture more accurately the structures inherent in the data, leading ultimately to better empirical performance. This book is not intended to be an encyclopedic compilation of the applications of Riemannian geometry. Instead, it focuses on several important research directions that are currently actively pursued by researchers in the field. These include statistical modeling and analysis on manifolds,optimization on manifolds, Riemannian manifolds and kernel methods, and dictionary learning and sparse coding on manifolds. Examples of applications include novel algorithms for Monte Carlo sampling and Gaussian Mixture Model fitting, 3D brain image analysis,image classification, action recognition, and motion tracking.
Algorithmic and Computational Robotics: New Directions 2000 WAFR
by Bruce Randall Donald Kevin M. Lynch Daniela RusAlgorithms that control the computational processes relating sensors and actuators are indispensable for robot navigation and the perception of the world in which they move. Therefore, a deep understanding of how algorithms work to achieve this control is essential for the development of efficient and usable robots in a broad field of applications.
Algorithmic and Experimental Methods in Algebra, Geometry, and Number Theory
by Wolfram Decker Gebhard Böckle Gunter Malle<P>This book presents state-of-the-art research and survey articles that highlight work done within the Priority Program SPP 1489 “Algorithmic and Experimental Methods in Algebra, Geometry and Number Theory”, which was established and generously supported by the German Research Foundation (DFG) from 2010 to 2016. The goal of the program was to substantially advance algorithmic and experimental methods in the aforementioned disciplines, to combine the different methods where necessary, and to apply them to central questions in theory and practice. Of particular concern was the further development of freely available open source computer algebra systems and their interaction in order to create powerful new computational tools that transcend the boundaries of the individual disciplines involved. <P> The book covers a broad range of topics addressing the design and theoretical foundations, implementation and the successful application of algebraic algorithms in order to solve mathematical research problems. <P> It offers a valuable resource for all researchers, from graduate students through established experts, who are interested in the computational aspects of algebra, geometry, and/or number theory.
Algorithmic and Geometric Aspects of Robotics (Routledge Revivals)
by Jacob T. Schwartz Chee-Keng YapFirst published in 1987, the seven chapters that comprise this book review contemporary work on the geometric side of robotics. The first chapter defines the fundamental goal of robotics in very broad terms and outlines a research agenda each of whose items constitutes a substantial area for further research. The second chapter presents recently developed techniques that have begun to address the geometric side of this research agenda and the third reviews several applied geometric ideas central to contemporary work on the problem of motion planning. The use of Voronoi diagrams, a theme opened in these chapters, is explored further later in the book. The fourth chapter develops a theme in computational geometry having obvious significance for the simplification of practical robotics problems — the approximation or decomposition of complex geometric objects into simple ones. The final chapters treat two examples of a class of geometric ‘reconstruction’ problem that have immediate application to computer-aided geometric design systems.
Algorithmic Aspects in Information and Management: 18th International Conference, AAIM 2024, Virtual Event, September 21–23, 2024, Proceedings, Part II (Lecture Notes in Computer Science #15180)
by Zhao Zhang Smita GhoshThis two-volume set LNCS 15179-15180 constitutes the refereed proceedings of the 18th International Conference on Algorithmic Aspects in Information and Management, AAIM 2024, which took place virtually during September 21-23, 2024. The 45 full papers presented in these two volumes were carefully reviewed and selected from 76 submissions. The papers are organized in the following topical sections: Part I: Optimization and applications; submodularity, management and others, Part II: Graphs and networks; quantum and others.
Algorithmic Aspects in Information and Management: 18th International Conference, AAIM 2024, Virtual Event, September 21–23, 2024, Proceedings, Part I (Lecture Notes in Computer Science #15179)
by Zhao Zhang Smita GhoshThis two-volume set LNCS 15179-15180 constitutes the refereed proceedings of the 18th International Conference on Algorithmic Aspects in Information and Management, AAIM 2024, which took place virtually during September 21-23, 2024. The 45 full papers presented in these two volumes were carefully reviewed and selected from 76 submissions. The papers are organized in the following topical sections: Part I: Optimization and applications; submodularity, management and others, Part II: Graphs and networks; quantum and others.
Algorithmic Aspects of Discrete Choice in Convex Optimization (Mathematische Optimierung und Wirtschaftsmathematik | Mathematical Optimization and Economathematics)
by David MüllerThis book develops a framework to analyze algorithmic aspects of discrete choice models in convex optimization. The central aspect is to derive new prox-functions from discrete choice surplus functions, which are then incorporated into convex optimization schemes. The book provides further economic applications of discrete choice prox-functions within the context of convex optimization such as network manipulation based on alternating minimization and dynamic pricing for online marketplaces.
Algorithmic Combinatorics: In Honour of Peter Paule on his 60th Birthday (Texts & Monographs in Symbolic Computation)
by Veronika Pillwein Carsten SchneiderThe book is centered around the research areas of combinatorics, special functions, and computer algebra. What these research fields share is that many of their outstanding results do not only have applications in Mathematics, but also other disciplines, such as computer science, physics, chemistry, etc. A particular charm of these areas is how they interact and influence one another. For instance, combinatorial or special functions' techniques have motivated the development of new symbolic algorithms. In particular, first proofs of challenging problems in combinatorics and special functions were derived by making essential use of computer algebra. This book addresses these interdisciplinary aspects. Algorithmic aspects are emphasized and the corresponding software packages for concrete problem solving are introduced. Readers will range from graduate students, researchers to practitioners who are interested in solving concrete problems within mathematics and other research disciplines.
Algorithmic Combinatorics on Partial Words
by Francine Blanchet-SadriThe discrete mathematics and theoretical computer science communities have recently witnessed explosive growth in the area of algorithmic combinatorics on words. The next generation of research on combinatorics of partial words promises to have a substantial impact on molecular biology, nanotechnology, data communication, and DNA computing. Delving
Algorithmic Cryptanalysis (Chapman & Hall/CRC Cryptography and Network Security Series)
by Antoine JouxIllustrating the power of algorithms, Algorithmic Cryptanalysis describes algorithmic methods with cryptographically relevant examples. Focusing on both private- and public-key cryptographic algorithms, it presents each algorithm either as a textual description, in pseudo-code, or in a C code program.Divided into three parts, the book begins with a
Algorithmic Decision Making with Python Resources: From Multicriteria Performance Records to Decision Algorithms via Bipolar-Valued Outranking Digraphs (International Series in Operations Research & Management Science #324)
by Raymond BisdorffThis book describes Python3 programming resources for implementing decision aiding algorithms in the context of a bipolar-valued outranking approach. These computing resources, made available under the name Digraph3, are useful in the field of Algorithmic Decision Theory and more specifically in outranking-based Multiple-Criteria Decision Aiding (MCDA). The first part of the book presents a set of tutorials introducing the Digraph3 collection of Python3 modules and its main objects, such as bipolar-valued digraphs and outranking digraphs. In eight methodological chapters, the second part illustrates multiple-criteria evaluation models and decision algorithms. These chapters are largely problem-oriented and demonstrate how to edit a new multiple-criteria performance tableau, how to build a best choice recommendation, how to compute the winner of an election and how to make rankings or ratings using incommensurable criteria. The book’s third part presents three real-world decision case studies, while the fourth part addresses more advanced topics, such as computing ordinal correlations between bipolar-valued outranking digraphs, computing kernels in bipolar-valued digraphs, testing for confidence or stability of outranking statements when facing uncertain or solely ordinal criteria significance weights, and tempering plurality tyranny effects in social choice problems. The fifth and last part is more specifically focused on working with undirected graphs, tree graphs and forests. The closing chapter explores comparability, split, interval and permutation graphs. The book is primarily intended for graduate students in management sciences, computational statistics and operations research. The chapters presenting algorithms for ranking multicriteria performance records will be of computational interest for designers of web recommender systems. Similarly, the relative and absolute quantile-rating algorithms, discussed and illustrated in several chapters, will be of practical interest to public and private performance auditors.
Algorithmic Decision Theory: 8th International Conference, ADT 2024, New Brunswick, NJ, USA, October 14–16, 2024, Proceedings (Lecture Notes in Computer Science #15248)
by Rupert Freeman Nicholas MatteiThis book constitutes the conference proceedings of the 8th International Conference on Algorithmic Decision Theory, ADT 2024, held in New Brunswick, NJ, USA, during October 14-16, 2024. The 18 full papers and 8 one-page abstracts presented were carefully selected from 39 submissions. The papers cover most of the major aspects of algorithmic decision theory, such as preference modeling and elicitation, voting, preference aggregation, fair division and resource allocation, coalition formation, game theory, and matching.
Algorithmic Differentiation in Finance Explained (Financial Engineering Explained)
by Marc HenrardThis book provides the first practical guide to the function and implementation of algorithmic differentiation in finance. Written in a highly accessible way, Algorithmic Differentiation Explained will take readers through all the major applications of AD in the derivatives setting with a focus on implementation. Algorithmic Differentiation (AD) has been popular in engineering and computer science, in areas such as fluid dynamics and data assimilation for many years. Over the last decade, it has been increasingly (and successfully) applied to financial risk management, where it provides an efficient way to obtain financial instrument price derivatives with respect to the data inputs. Calculating derivatives exposure across a portfolio is no simple task. It requires many complex calculations and a large amount of computer power, which in prohibitively expensive and can be time consuming. Algorithmic differentiation techniques can be very successfully in computing Greeks and sensitivities of a portfolio with machine precision. Written by a leading practitioner who works and programmes AD, it offers a practical analysis of all the major applications of AD in the derivatives setting and guides the reader towards implementation. Open source code of the examples is provided with the book, with which readers can experiment and perform their own test scenarios without writing the related code themselves.
Algorithmic Game Theory: 10th International Symposium, SAGT 2017, L’Aquila, Italy, September 12–14, 2017, Proceedings (Lecture Notes in Computer Science #10504)
by Vittorio Bilò Michele FlamminiThis book constitutes the refereed proceedings of the 10th International Symposium on Algorithmic Game Theory, SAGT 2017, held in L'Aquila, Italy, in September 2017.The 30 full papers presented were carefully reviewed and selected from 66 submissions. The papers cover various important aspects of algorithmic game theory such as auctions, computational aspects of games, congestion games, network and opinion formation games, mechanism design, incentives and regret minimization, and resource allocation.
Algorithmic Game Theory: 9th International Symposium, SAGT 2016, Liverpool, UK, September 19–21, 2016, Proceedings (Lecture Notes in Computer Science #9928)
by Martin Gairing Rahul SavaniThis book constitutes the refereed proceedings of the 9th International Symposium on Algorithmic Game Theory, SAGT 2016, held in Liverpool, UK, in September 2016. The 26 full papers presented together with 2 one-page abstracts were carefully reviewed and selected from 62 submissions. The accepted submissions cover various important aspectsof algorithmic game theory such as computational aspects of games, congestion games and networks, matching and voting, auctions and markets, and mechanism design.
Algorithmic Game Theory: 8th International Symposium, SAGT 2015, Saarbrücken, Germany, September 28-30, 2015. Proceedings (Lecture Notes in Computer Science #9347)
by Martin HoeferThis book constitutes the refereed proceedings of the 8th International Symposium on Algorithmic Game Theory, SAGT 2015, held in Saarbrücken, Germany, in September 2015. The 22 full papers presented together with one extended abstract and 6 brief announcements were carefully reviewed and selected from 63 submissions. They cover various important aspects of algorithmic game theory, such as matching under preferences; cost sharing; mechanism design and social choice; auctions; networking; routing and fairness; and equilibrium computation.
Algorithmic Governance: Politics and Law in the Post-Human Era
by Ignas KalpokasThis book analyses the changes to the regulation of everyday life that have taken place as a result of datafication, the ever-growing analytical, predictive, and structuring role of algorithms, and the prominence of the platform economy. This new form of regulation – algorithmic governance – ranges from nudging individuals towards predefined outcomes to outright structuration of behaviour through digital architecture. The author reveals the strength and pervasiveness of algorithmic politics through a comparison with the main traditional form of regulation: law. These changes are subsequently demonstrated to reflect a broader shift away from anthropocentric accounts of the world. In doing so, the book adopts a posthumanist framework which focuses on deep embeddedness and interactions between humans, the natural environment, technology, and code.
Algorithmic Learning in a Random World
by Alexander Gammerman Vladimir Vovk Glenn ShaferThis book is about conformal prediction, an approach to prediction that originated in machine learning in the late 1990s. The main feature of conformal prediction is the principled treatment of the reliability of predictions. The prediction algorithms described — conformal predictors — are provably valid in the sense that they evaluate the reliability of their own predictions in a way that is neither over-pessimistic nor over-optimistic (the latter being especially dangerous). The approach is still flexible enough to incorporate most of the existing powerful methods of machine learning. The book covers both key conformal predictors and the mathematical analysis of their properties. Algorithmic Learning in a Random World contains, in addition to proofs of validity, results about the efficiency of conformal predictors. The only assumption required for validity is that of "randomness" (the prediction algorithm is presented with independent and identically distributed examples); in later chapters, even the assumption of randomness is significantly relaxed. Interesting results about efficiency are established both under randomness and under stronger assumptions. Since publication of the First Edition in 2005 conformal prediction has found numerous applications in medicine and industry, and is becoming a popular machine-learning technique. This Second Edition contains three new chapters. One is about conformal predictive distributions, which are more informative than the set predictions produced by standard conformal predictors. Another is about the efficiency of ways of testing the assumption of randomness based on conformal prediction. The third new chapter harnesses conformal testing procedures for protecting machine-learning algorithms against changes in the distribution of the data. In addition, the existing chapters have been revised, updated, and expanded.
Algorithmic Lie Theory for Solving Ordinary Differential Equations (Chapman & Hall/CRC Pure and Applied Mathematics)
by Fritz SchwarzDespite the fact that Sophus Lie's theory was virtually the only systematic method for solving nonlinear ordinary differential equations (ODEs), it was rarely used for practical problems because of the massive amount of calculations involved. But with the advent of computer algebra programs, it became possible to apply Lie theory to concrete proble
Algorithmic Mathematics
by Stefan Hougardy Jens VygenAlgorithms play an increasingly important role in nearly all fields of mathematics. This book allows readers to develop basic mathematical abilities, in particular those concerning the design and analysis of algorithms as well as their implementation. It presents not only fundamental algorithms like the sieve of Eratosthenes, the Euclidean algorithm, sorting algorithms, algorithms on graphs, and Gaussian elimination, but also discusses elementary data structures, basic graph theory, and numerical questions. In addition, it provides an introduction to programming and demonstrates in detail how to implement algorithms in C++. This textbook is suitable for students who are new to the subject and covers a basic mathematical lecture course, complementing traditional courses on analysis and linear algebra. Both authors have given this "Algorithmic Mathematics" course at the University of Bonn several times in recent years.
Algorithmic Trading and Quantitative Strategies
by Raja VeluAlgorithmic Trading and Quantitative Strategies provides an in-depth overview of this growing field with a unique mix of quantitative rigor and practitioner’s hands-on experience. The focus on empirical modeling and practical know-how makes this book a valuable resource for students and professionals. The book starts with the often overlooked context of why and how we trade via a detailed introduction to market structure and quantitative microstructure models. The authors then present the necessary quantitative toolbox including more advanced machine learning models needed to successfully operate in the field. They next discuss the subject of quantitative trading, alpha generation, active portfolio management and more recent topics like news and sentiment analytics. The last main topic of execution algorithms is covered in detail with emphasis on the state of the field and critical topics including the elusive concept of market impact. The book concludes with a discussion on the technology infrastructure necessary to implement algorithmic strategies in large-scale production settings. A git-hub repository includes data-sets and explanatory/exercise Jupyter notebooks. The exercises involve adding the correct code to solve the particular analysis/problem.