Moshe Haviv: Continuous Optimization For Data Science, Gebunden
Continuous Optimization For Data Science
(soweit verfügbar beim Lieferanten)
- Verlag:
- World Scientific, 07/2025
- Einband:
- Gebunden
- Sprache:
- Englisch
- ISBN-13:
- 9789811299193
- Artikelnummer:
- 12074853
- Umfang:
- 320 Seiten
- Gewicht:
- 594 g
- Maße:
- 229 x 152 mm
- Stärke:
- 19 mm
- Erscheinungstermin:
- 7.7.2025
- Hinweis
-
Achtung: Artikel ist nicht in deutscher Sprache!
Klappentext
The text is divided into three main parts: unconstrained optimization, constrained optimization, and linear programming. The first part addresses unconstrained optimization in single-variable and multivariable functions, introducing key algorithms such as steepest descent, Newton, and quasi-Newton methods.
The second part focuses on constrained optimization, starting with linear equality constraints and extending to more general cases, including inequality constraints. It details optimality conditions, sensitivity analysis, and relevant algorithms for solving these problems.
The third part covers linear programming, presenting the formulation of LP problems, the simplex algorithm, and sensitivity analysis. Throughout, the text provides numerous applications to data science, such as linear regression, maximum likelihood estimation, expectation-maximization algorithms, support vector machines, and linear neural networks.
Biografie
Moshe Haviv is a Professor of Statistics and Department Head at the Hebrew University in Jerusalem, Israel. He received his B.Sc. in Mathematics at Tel Aviv University, and his M.A. in Administrative Sciences and Ph.D. in Operations Research/Management Science both at Yale University. His research interests include Operations Research, Queueing Models, decision making and strategic behavior in queues, Markov decision processes, and large Markov chains. He is a member of the Center for Rationality at the Hebrew University, and is a visiting professor (summers) in Operations Management and Econometrics at the University of Sydney.
