Nonlinear Conjugate Gradient Methods for Unconstrained Optimization by Neculai Andrei

Nonlinear Conjugate Gradient Methods for Unconstrained Optimization by Neculai Andrei

Two approaches are known for solving large-scale unconstrained optimization problemsthe limited-memory quasi-Newton method (truncated Newton method) and the conjugate gradient method.

“The book is well written for understanding several kinds of nonlinear CG methods and their
convergence properties… The book will be very useful for researchers, graduate students and practitioners interested in studying nonlinear CG methods.” (Hiroshi Yabe, Mathematical Reviews, April, 2022)

Neculai Andrei holds a position at the Center for Advanced Modeling and Optimization at the Academy of Romanian Scientists in Bucharest, Romania. Dr. Andrei’s areas of interest include mathematical modeling, linear programming, nonlinear optimization, high performance computing, and numerical methods in mathematical programming. In addition to this present volume, Neculai Andrei has published 2 books with Springer including Continuous Nonlinear Optimization for Engineering Applications in GAMS Technology (2017) and Nonlinear Optimization Applications Using the GAMS Technology (2013).

SKU	Unavailable
ISBN 13	9783030429492
ISBN 10	3030429490
Title	Nonlinear Conjugate Gradient Methods for Unconstrained Optimization
Author	Neculai Andrei
Series	Springer Optimization And Its Applications
Condition	Unavailable
Binding Type	Hardback
Publisher	Springer Nature Switzerland AG
Year published	2020-06-24
Number of pages	498
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.