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hardcover. Condición: Good. Brad Davies Ilustrador. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority!

Condición: very_good. Brad Davies Ilustrador. Gently read. May have name of previous ownership, or ex-library edition. Binding tight; spine straight and smooth, with no creasing; covers clean and crisp. Minimal signs of handling or shelving. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item! Ships USPS Media Mail.

Condición: Interior is excellent. Light wear to cover.

Condición: New.

Soft cover. Condición: New.

Condición: very_good. Gently read. May have name of previous ownership, or ex-library edition. Binding tight; spine straight and smooth, with no creasing; covers clean and crisp. Minimal signs of handling or shelving. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item! Ships USPS Media Mail.

Condición: As New. Unread book in perfect condition.

paperback. Condición: VeryGood. A copy that may have been read, very minimal wear and tear. May have a remainder mark.

Oprawa wydawnicza tekturowa. Condición: Pozycja w ladnym stanie. @ Blurb / Notka wydawnicza: Warszawa: Wydawnictwa Naukowo-Techniczne, 2008; @ Size of the book block / Wymiar bloku: 25 cm; @ Series / Seria: (Seria: Zabytki Polski; 1); @ Circulation / Naklad: -; @ Pages / Strony: 61, [3]s.; @ Thematic categories / Kategorie tematyczne: historia Polski miejscowosci i regiony - h. Polski, mazowieckie Wegrow, regiony Mazowsze / history of Poland, , regions Mazovia artefacts / Geschichte Polens, , Regionen Masowien;

Paperback or Softback. Condición: New. Hands-On Mathematical Optimization with Python. Book.

Condición: New. 1st edition NO-PA16APR2015-KAP.

Condición: New.

Paperback. Condición: New. This practical guide to optimization combines mathematical theory with hands-on coding examples to explore how Python can be used to model problems and obtain the best possible solutions. Presenting a balance of theory and practical applications, it is the ideal resource for upper-undergraduate and graduate students in applied mathematics, data science, business, industrial engineering and operations research, as well as practitioners in related fields. Beginning with an introduction to the concept of optimization, this text presents the key ingredients of an optimization problem and the choices one needs to make when modeling a real-life problem mathematically. Topics covered range from linear and network optimization to convex optimization and optimizations under uncertainty. The book's Python code snippets, alongside more than 50 Jupyter notebooks on the author's GitHub, allow students to put the theory into practice and solve problems inspired by real-life challenges, while numerous exercises sharpen students' understanding of the methods discussed.

Condición: New.

paperback. Condición: New.

Condición: New. In.

Condición: New.

Condición: New. 2025. paperback. . . . . .

Condición: As New. Unread book in perfect condition.

Condición: New. 2025. paperback. . . . . . Books ship from the US and Ireland.

Paperback. Condición: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.

Paperback. Condición: Brand New. 387 pages. 10.00x7.01x0.50 inches. In Stock.

Paperback. Condición: new. Paperback. This practical guide to optimization combines mathematical theory with hands-on coding examples to explore how Python can be used to model problems and obtain the best possible solutions. Presenting a balance of theory and practical applications, it is the ideal resource for upper-undergraduate and graduate students in applied mathematics, data science, business, industrial engineering and operations research, as well as practitioners in related fields. Beginning with an introduction to the concept of optimization, this text presents the key ingredients of an optimization problem and the choices one needs to make when modeling a real-life problem mathematically. Topics covered range from linear and network optimization to convex optimization and optimizations under uncertainty. The book's Python code snippets, alongside more than 50 Jupyter notebooks on the author's GitHub, allow students to put the theory into practice and solve problems inspired by real-life challenges, while numerous exercises sharpen students' understanding of the methods discussed. A hands-on Python-based guide to mathematical optimization for undergraduates and graduates in applied mathematics, industrial engineering and operations research, as well as practitioners in related fields. Focuses on practical applications, with over 50 Jupyter notebooks and extensive exercises to test understanding. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This practical guide to optimization combines mathematical theory with hands-on coding examples to explore how Python can be used to model problems and obtain the best possible solutions. Presenting a balance of theory and practical applications, it is the ideal resource for upper-undergraduate and graduate students in applied mathematics, data science, business, industrial engineering and operations research, as well as practitioners in related fields. Beginning with an introduction to the concept of optimization, this text presents the key ingredients of an optimization problem and the choices one needs to make when modeling a real-life problem mathematically. Topics covered range from linear and network optimization to convex optimization and optimizations under uncertainty. The book's Python code snippets, alongside more than 50 Jupyter not Elektronisches Buch on the author's GitHub, allow students to put the theory into practice and solve problems inspired by real-life challenges, while numerous exercises sharpen students' understanding of the methods discussed.

Paperback. Condición: new. Paperback. This practical guide to optimization combines mathematical theory with hands-on coding examples to explore how Python can be used to model problems and obtain the best possible solutions. Presenting a balance of theory and practical applications, it is the ideal resource for upper-undergraduate and graduate students in applied mathematics, data science, business, industrial engineering and operations research, as well as practitioners in related fields. Beginning with an introduction to the concept of optimization, this text presents the key ingredients of an optimization problem and the choices one needs to make when modeling a real-life problem mathematically. Topics covered range from linear and network optimization to convex optimization and optimizations under uncertainty. The book's Python code snippets, alongside more than 50 Jupyter notebooks on the author's GitHub, allow students to put the theory into practice and solve problems inspired by real-life challenges, while numerous exercises sharpen students' understanding of the methods discussed. A hands-on Python-based guide to mathematical optimization for undergraduates and graduates in applied mathematics, industrial engineering and operations research, as well as practitioners in related fields. Focuses on practical applications, with over 50 Jupyter notebooks and extensive exercises to test understanding. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Condición: New. This practical guide to optimization combines mathematical theory with hands-on coding examples to explore how Python can be used to model problems and obtain the best possible solutions. Presenting a balance of theory and practical applications, it is the ideal resource for upper-undergraduate and graduate students in applied mathematics, data science, business, industrial engineering and operations research, as well as practitioners in related fields. Beginning with an introduction to the concept of optimization, this text presents the key ingredients of an optimization problem and the choices one needs to make when modeling a real-life problem mathematically. Topics covered range from linear and network optimization to convex optimization and optimizations under uncertainty. The book's Python code snippets, alongside more than 50 Jupyter notebooks on the author's GitHub, allow students to put the theory into practice and solve problems inspired by real-life challenges, while numerous exercises sharpen students' understanding of the methods discussed.

Paperback. Condición: Fair. No Jacket. Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less.

Hardcover. Condición: New.

Condición: as new. Tilburg : CentER, Tilburg University, 2017. Paperback. 254 pp. Condition : as new copy. ISBN 9789056685041. Keywords : ECONOMICS,

Soft cover. Condición: Very Good. 697 pages in very good condition. SPIE Proceedings Series Volume 1673. Pages are clean and unmarked with black and white diagrams and illustrations throughout. Page edges are lightly smudged. Bound in yellow wraps with black titles. Lightly wrinkled and smudged on the spine, light wear around the edges. VG Size: 8 1/2 x 11. Book.