{"title":"Charles Audet","description":"\u003cp\u003e\u0026lt;p\u0026gt;\u0026amp;nbsp;\u0026lt;\/p\u0026gt;\u003cbr\u003e\u0026lt;p class=\"MsoNormal\"\u0026gt;\u0026lt;strong style=\"mso-bidi-font-weight: normal;\"\u0026gt;\u0026lt;span style=\"mso-ansi-language: EN-IN;\"\u0026gt;Dr. Charles Audet\u0026lt;\/span\u0026gt;\u0026lt;\/strong\u0026gt;\u0026lt;span style=\"mso-ansi-language: EN-IN;\"\u0026gt; is a Professor of Mathematics at the \u0026amp;Eacute;cole Polytechnique de Montr\u0026amp;eacute;al. His research interests include the analysis and development of algorithms for blackbox nonsmooth optimization, and structured global optimization. He obtained a Ph.D. degree in applied mathematics from the \u0026amp;Eacute;cole Polytechnique de Montr\u0026amp;eacute;al, and worked as a postdoctoral researcher at Rice University.\u0026lt;\/span\u0026gt;\u0026lt;\/p\u0026gt;\u003cbr\u003e\u0026lt;p\u0026gt;\u0026lt;strong style=\"mso-bidi-font-weight: normal;\"\u0026gt;\u0026lt;span style=\"font-size: 11.0pt; line-height: 105%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;\"\u0026gt;Dr. Warren Hare\u0026lt;\/span\u0026gt;\u0026lt;\/strong\u0026gt;\u0026lt;span style=\"font-size: 11.0pt; line-height: 105%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;\"\u0026gt; is a Professor of Mathematics at the University of British Columbia, Okanagan Campus.\u0026lt;span style=\"mso-spacerun: yes;\"\u0026gt;\u0026amp;nbsp; \u0026lt;\/span\u0026gt;His research interests include numerical analysis and algorithm design, particularly for derivative-free optimisation.\u0026lt;span style=\"mso-spacerun: yes;\"\u0026gt;\u0026amp;nbsp; \u0026lt;\/span\u0026gt;He obtained his Ph.D. in optimization from Simon Fraser University and worked as postdoctoral researcher at the Instituto de Mathem\u0026amp;aacute;tica Pura e Applicada and McMaster University.\u0026lt;\/span\u0026gt;\u0026lt;\/p\u0026gt;\u003cbr\u003e\u0026lt;p\u0026gt;\u0026amp;nbsp;\u0026lt;\/p\u0026gt;\u003cbr\u003e\u0026lt;p\u0026gt;\u0026amp;nbsp;\u0026lt;\/p\u0026gt;\u003cbr\u003e\u0026lt;p\u0026gt;\u0026amp;nbsp;\u0026lt;\/p\u0026gt;\u003c\/p\u003e","products":[{"product_id":"derivative-free-and-blackbox-optimization-charles-audet-ebook","title":"Derivative-Free and Blackbox Optimization","description":"\u003cp class=\"MsoNormal\"\u003e\u003cspan style=\"mso-ansi-language: EN-IN;\"\u003eThe second edition of Derivative-Free and Blackbox Optimization offers a comprehensive introduction to the field of optimization when derivatives are unavailable, unreliable, or impractical. Whether you’re a student, instructor, or self-learner, this book is designed to guide you through both the foundations and advanced techniques of derivative-free and blackbox optimization. This new edition features significantly expanded exercises, updated and intuitive notation, over 30 new figures, and a wide range of pedagogical enhancements aimed at making complex concepts accessible and engaging. The book is structured into five parts. Part 1 established foundational principles, including an expanded chapter on proper benchmarking. Parts 2, 3, and 4, take an in-depth look at heuristics, direct search, and model based approaches (respectively). Part 5 extends these approaches to specialised settings. Finally, a new appendix contributed by \u003c\/span\u003e\u003cspan lang=\"EN-US\"\u003eSébastien Le Digabel\u003c\/span\u003e\u003cspan style=\"mso-ansi-language: EN-IN;\"\u003e, details several real-world applications of blackbox optimization, and links to software for each application. Whether used in the classroom or for independent exploration, this book is a powerful resource for understanding and applying optimization methods – no gradients required. \u003c\/span\u003e\u003c\/p\u003e","brand":"Charles Audet","offers":[{"title":"Default Title","offer_id":54188005818695,"sku":"9783032009067","price":69.54,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0920\/5455\/2903\/files\/derivative-free-and-blackbox-optimization-ebook-cover.webp?v=1781808909"}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0920\/5455\/2903\/collections\/charles-audet-autor-kollektion.webp?v=1781808906","url":"https:\/\/www.cinebuch.de\/collections\/charles-audet.oembed","provider":"CineBuch","version":"1.0","type":"link"}