Kenneth Lange:Optimization
- neues Buch 2019, ISBN: 9781461458371
[ED: Buch], [PU: Springer New York], Neuware - Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analyti… Mehr…
[ED: Buch], [PU: Springer New York], Neuware - 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 a balance between presentation of mathematical theory and development of numerical algorithms. Building on students' skills in calculus and linear algebra, the text provides a rigorous exposition without undue abstraction. Its stress on statistical applications will be especially appealing to graduate students of statistics and biostatistics. The intended audience also includes students in applied mathematics, computational biology, computer science, economics, and physics who want to see rigorous mathematics combined with real applications.In this second edition the emphasis remains on finite-dimensional optimization. New material has been added on the MM algorithm, block descent and ascent, and the calculus of variations. Convex calculus is now treated in much greater depth. Advanced topics such as the Fenchel conjugate, subdifferentials, duality, feasibility, alternating projections, projected gradient methods, exact penalty methods, and Bregman iteration will equip students with the essentials for understanding modern data mining techniques in high dimensions. - Besorgungstitel - vorauss. Lieferzeit 3-5 Tage., DE, [SC: 0.00], Neuware, gewerbliches Angebot, 241x160x33 mm, 548, [GW: 980g], Banküberweisung, Offene Rechnung, Kreditkarte, PayPal, Offene Rechnung (Vorkasse vorbehalten), Internationaler Versand<
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Optimization Kenneth Lange Author
- neues BuchISBN: 9781461458371
Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically. This introduction to optimization attemp… Mehr…
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 a balance between presentation of mathematical theory and development of numerical algorithms. Building on students’ skills in calculus and linear algebra, the text provides a rigorous exposition without undue abstraction. Its stress on statistical applications will be especially appealing to graduate students of statistics and biostatistics. The intended audience also includes students in applied mathematics, computational biology, computer science, economics, and physics who want to see rigorous mathematics combined with real applications. In this second edition the emphasis remains on finite-dimensional optimization. New material has been added on the MM algorithm, block descent and ascent, and the calculus of variations. Convex calculus is now treated in much greater depth. Advanced topics such as the Fenchel conjugate, subdifferentials, duality, feasibility, alternating projections, projected gradient methods, exact penalty methods, and Bregman iteration will equip students with the essentials for understanding modern data mining techniques in high dimensions. New Textbooks>Hardcover>Science>Statistics & Probability>Statistics & Probability, Springer New York Core >2 >T<
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Lange, Kenneth:Optimization / Kenneth Lange / Buch / Springer Texts in Statistics / HC gerader Rücken kaschiert / XVII / Englisch / 2013 / Springer New York / EAN 9781461458371
- gebunden oder broschiert 2013, ISBN: 9781461458371
[ED: Gebunden], [PU: Springer New York], Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically.… Mehr…
[ED: Gebunden], [PU: Springer New York], 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 a balance between presentation of mathematical theory and development of numerical algorithms. Building on students¿ skills in calculus and linear algebra, the text provides a rigorous exposition without undue abstraction. Its stress on statistical applications will be especially appealing to graduate students of statistics and biostatistics. The intended audience also includes students in applied mathematics, computational biology, computer science, economics, and physics who want to see rigorous mathematics combined with real applications. In this second edition the emphasis remains on finite-dimensional optimization. New material has been added on the MM algorithm, block descent and ascent, and the calculus of variations. Convex calculus is now treated in much greater depth. Advanced..., DE, [SC: 0.00], Neuware, gewerbliches Angebot, 548, [GW: 980g], 2nd ed. 2013, Banküberweisung, PayPal, Klarna-Sofortüberweisung, [CT: Sonstiges / Sonstiges]<
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Lange, Kenneth:Optimization | Kenneth Lange | Buch | Springer Texts in Statistics | HC gerader Rücken kaschiert | XVII | Englisch | 2013 | Springer New York | EAN 9781461458371
- gebunden oder broschiert 2013, ISBN: 9781461458371
[ED: Gebunden], [PU: Springer New York], Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically.… Mehr…
[ED: Gebunden], [PU: Springer New York], 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 a balance between presentation of mathematical theory and development of numerical algorithms. Building on students¿ skills in calculus and linear algebra, the text provides a rigorous exposition without undue abstraction. Its stress on statistical applications will be especially appealing to graduate students of statistics and biostatistics. The intended audience also includes students in applied mathematics, computational biology, computer science, economics, and physics who want to see rigorous mathematics combined with real applications. In this second edition the emphasis remains on finite-dimensional optimization. New material has been added on the MM algorithm, block descent and ascent, and the calculus of variations. Convex calculus is now treated in much greater depth. Advanced..., DE, [SC: 0.00], Neuware, gewerbliches Angebot, 548, [GW: 980g], 2nd ed. 2013, Banküberweisung, PayPal, Klarna-Sofortüberweisung, [CT: Sonstiges / Sonstiges]<
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Optimization
- neues BuchISBN: 9781461458371
Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically. This introduction to optimization attemp… Mehr…
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 a balance between presentation of mathematical theory and development of numerical algorithms. Building on students’ skills in calculus and linear algebra, the text provides a rigorous exposition without undue abstraction. Its stress on statistical applications will be especially appealing to graduate students of statistics and biostatistics. The intended audience also includes students in applied mathematics, computational biology, computer science, economics, and physics who want to see rigorous mathematics combined with real applications. In this second edition the emphasis remains on finite-dimensional optimization. New material has been added on the MM algorithm, block descent and ascent, and the calculus of variations. Convex calculus is now treated in much greater depth. Advanced topics such as the Fenchel conjugate, subdifferentials, duality, feasibility, alternating projections, projected gradient methods, exact penalty methods, and Bregman iteration will equip students with the essentials for understanding modern data mining techniques in high dimensions. , Springer<
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