Data Science and Optimization (Hardcover). Este artículo no está disponible.
Idioma: inglés
Editorial: Springer Nature Switzerland AG, Cham, 2026
- Tapa dura
- Nuevo

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Descripción del artículo del vendedor
Hardcover. Data science and optimization are increasingly intertwined as both focus on developing computational and methodological approaches to tackling large and otherwise complex datasets. Optimization is primarily concerned with accuracy, computational efficiency, and robustness while data science emphasizes achieving effective results on real datasets. Although some data science approaches involve the implicit optimization of objective functions, there remains a dearth of work that brings advanced optimization techniques to bear on data science problems. The goal of the Fields Focus Program on Data Science and Optimization held in November 2019 at the Fields Institute in Toronto, was to bring together researchers in data science and optimization, both theoretical and applied, in an effort to bridge the fields and stimulate cross-disciplinary interaction and collaboration.In the spirit of the program, this volume compiles recent development and connections in the fields of data science and optimization, and the ways in which they overlap. It features novel results and state-of-the-art surveys as well as open problems. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…
N° de ref. del artículo 9783032038432
- Título
- Data Science and Optimization (Hardcover)
- Autor
- Sanjeena Dang
- Editorial
- Springer Nature Switzerland AG, Cham
- Año de publicación
- 2026
- Estado
- new
- Encuadernación
- Hardcover
- Idioma
- inglés
- ISBN 10
- 303203843X
- ISBN 13
- 9783032038432
Data science and optimization are increasingly intertwined as both focus on developing computational and methodological approaches to tackling large and otherwise complex datasets. Optimization is primarily concerned with accuracy, computational efficiency, and robustness while data science emphasizes achieving effective results on real datasets. Although some data science approaches involve the implicit optimization of objective functions, there remains a dearth of work that brings advanced optimization techniques to bear on data science problems. The goal of the Fields Focus Program on Data Science and Optimization held in November 2019 at the Fields Institute in Toronto, was to bring together researchers in data science and optimization, both theoretical and applied, in an effort to bridge the fields and stimulate cross-disciplinary interaction and collaboration.
In the spirit of the program, this volume compiles recent development and connections in the fields of data science and optimization, and the ways in which they overlap. It features novel results and state-of-the-art surveys as well as open problems.
“Sinopsis” puede pertenecer a otra edición de este título.
De la contraportada
Data science and optimization are increasingly intertwined as both focus on developing computational and methodological approaches to tackling large and otherwise complex datasets. Optimization is primarily concerned with accuracy, computational efficiency, and robustness while data science emphasizes achieving effective results on real datasets. Although some data science approaches involve the implicit optimization of objective functions, there remains a dearth of work that brings advanced optimization techniques to bear on data science problems. The goal of the Fields Focus Program on Data Science and Optimization held in November 2019 at the Fields Institute in Toronto, was to bring together researchers in data science and optimization, both theoretical and applied, in an effort to bridge the fields and stimulate cross-disciplinary interaction and collaboration.
In the spirit of the program, this volume compiles recent development and connections in the fields of data science and optimization, and the ways in which they overlap. It features novel results and state-of-the-art surveys as well as open problems.
“Acerca de” puede pertenecer a otra edición de este título.