Introduction to Stochastic Programming

Introduction to Stochastic Programming

François Louveaux / John R. Birge

81,08 €
IVA incluido
Disponible
Editorial:
Springer Nature B.V.
Año de edición:
2011
ISBN:
9781493937035
81,08 €
IVA incluido
Disponible

Selecciona una librería:

  • Librería Samer Atenea
  • Librería Aciertas (Toledo)
  • Kálamo Books
  • Librería Perelló (Valencia)
  • Librería Elías (Asturias)
  • Donde los libros
  • Librería Kolima (Madrid)
  • Librería Proteo (Málaga)

The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems.In this extensively updated new edition there is more material on methods and examples including several new approaches for discrete variables, new results on risk measures in modeling and Monte Carlo sampling methods, a new chapter on relationships to other methods including approximate dynamic programming, robust optimization and online methods.The book is highly illustrated with chapter summaries and many examples and exercises. Students, researchers and practitioners in operations research and the optimization area will find it particularly of interest. Review of First Edition:'The discussion on modeling issues, the large number of examples used to illustrate the material, and the breadth of the coverage make ’Introduction to Stochastic Programming’ an ideal textbook for the area.' (Interfaces, 1998)

Artículos relacionados

  • Bayesian Analysis with Python - Third Edition
    Osvaldo Martin
    Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these librariesKey Features- Conduct Bayesian data analysis with step-by-step guidance- Gain insight into a modern, practical, and computational approach to Bayesian statistical modeling- Enhance ...
    Disponible

    69,74 €

  • Python Machine Learning By Example - Fourth Edition
    Yuxi (Hayden) Liu
    Author Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandasKey Features:- Discover new and updated content on NLP transformers, PyTorch, and computer vision modeling- Includes a dedicated chapter on best practices and...
    Disponible

    65,97 €

  • Bayesian Analysis with Python - Third Edition
    Osvaldo Martin
    Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these librariesKey Features:- Conduct Bayesian data analysis with step-by-step guidance- Gain insight into a modern, practical, and computational approach to Bayesian statistical modeling- Enhance...
  • Mathematical Explorations with MATLAB
    K. Chen / KChen / Ke Chan / Ke Chen
    ...
    Disponible

    73,74 €

  • Smart Medical Imaging for Diagnosis and Treatment Planning
    This book presents advanced research on smart health technologies, focusing upon the innovative transformations in diagnosis and treatment planning using medical imaging and data analysed by data science techniques. It shows how smart health technologies leverage artificial intelligence (AI) and big data analytics. ...
  • Supervised Machine Learning
    Samuel Berestizhevsky / Tanya Kolosova
    AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. It comprises of bootstrapping to create multiple training and testing data sets, design and analysis of statistical experiments and optimal hyper-parameters for ML methods. ...
    Disponible

    92,50 €

Otros libros del autor

  • Introduction to Stochastic Programming
    François Louveaux / John R. Birge
    Introduction and Examples.- Uncertainty and Modeling Issues.- Basic Properties and Theory.- The Value of Information and the Stochastic Solution.- Two-Stage Recourse Problems.- Multistage Stochastic Programs.- Stochastic Integer Programs.- Evaluating and Approximating Expectations.- Monte Carlo Methods.- Multistage Approximations.- Sample Distribution Functions.- References. ...