{"title":"Books by Mykel J. Kochenderfer","description":null,"products":[{"product_id":"9780262039420","title":"Algorithms for Optimization","description":"\u003cb\u003eA comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems.\u003c\/b\u003e\u003cp\u003eThis book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language.\u003c\/p\u003e\u003cp\u003eTopics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.\u003c\/p\u003e","brand":"Gardners","offers":[{"title":"Default Title","offer_id":56230912065909,"sku":"9780262039420","price":96.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0612\/7193\/3106\/files\/9780262039420_09777c70-0b38-41f1-8e1d-65b016ef5f88.jpg?v=1786012255"},{"product_id":"9780262047012","title":"Algorithms for Decision Making","description":"\u003cb\u003eA broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eAutomated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them.\u003cbr\u003e \u003cbr\u003eThe book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.","brand":"Gardners","offers":[{"title":"Default Title","offer_id":56231200850293,"sku":"9780262047012","price":90.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0612\/7193\/3106\/files\/9780262047012_9d4f322e-fbb7-4b30-9d75-0140dbfc322f.jpg?v=1786015901"},{"product_id":"algorithms-for-validation","title":"Algorithms for Validation","description":"\u003cb\u003eA broad, rigorous introduction to algorithms for validating safety-critical systems.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eValidation is a critical component of the development process for decision-making systems used in high-stakes settings, from autonomous vehicles and aviation to finance and healthcare. As these systems and their operating environments increase in complexity, understanding the full spectrum of possible behaviors becomes more difficult and requires a rigorous validation process. This comprehensive textbook presents a variety of computational methods for validating autonomous systems, introducing the underlying mathematical problem formulations and the algorithms for solving them. Unifying techniques from multiple fields under a common validation framework, it provides advanced undergraduate and graduate students objective strategies for validation. \u003cbr\u003e\u003cbr\u003eThe text first covers techniques required to formulate validation problems in a common structure. It then addresses sampling-based failure analysis techniques such as falsification and failure probability estimation, followed by formal methods for reachability analysis, explainability, and runtime monitoring. Algorithmic implementations are provided throughout. \u003cbr\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003e Offers unified framework for formulating validation problems  \u003c\/li\u003e\n\u003cli\u003e Presents both sampling-based and formal methods  \u003c\/li\u003e\n\u003cli\u003e Accessibly introduces failure probability estimation algorithms and reachability algorithms for linear, nonlinear, and discrete systems \u003c\/li\u003e\n\u003cli\u003e Emphasizes practical considerations for applying algorithms to real-world systems\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Gardners","offers":[{"title":"Default Title","offer_id":57499247247733,"sku":"9780262056014","price":133.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0612\/7193\/3106\/files\/9780262056014.jpg?v=1786694565"},{"product_id":"algorithms-for-optimization-second-edition","title":"Algorithms for Optimization, second edition","description":"\u003cb\u003eA comprehensive, practical introduction to optimization updated with new chapters on duality, quadratic programming, and disciplined convex programming.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eThe extensively updated second edition of this popular textbook provides a comprehensive introduction to optimization with a focus on practical algorithms. Mykel Kochenderfer and Tim Wheeler approach optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. Suitable for advanced undergraduates and graduate students in mathematics, statistics, computer science, engineering, and operations research and as a reference for professionals, the text provides concrete implementations in the Julia programming language.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eSecond edition highlights:\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003eNew chapters on duality, quadratic      programming, and disciplined convex programming\u003c\/li\u003e\n\u003cli\u003eAdditional methods covered,      from new ways to estimate gradients to multifidelity techniques\u003c\/li\u003e\n\u003cli\u003eImproved accessibility,      expanded explanations, updated references, and streamlined algorithms\u003c\/li\u003e\n\u003cli\u003eMore exercises, example      applications, and figures\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Gardners","offers":[{"title":"Default Title","offer_id":57680202760565,"sku":"9780262058162","price":110.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0612\/7193\/3106\/files\/9780262058162.jpg?v=1792423418"}],"url":"https:\/\/backstory.london\/collections\/author-mykel-j-kochenderfer.oembed","provider":"Backstory","version":"1.0","type":"link"}