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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Nonlinear Conjugate Gradient Methods for Unconstrained Optimization

Nonlinear Conjugate Gradient Methods for Unconstrained Optimization

Andrei, Neculai

Paperback

Series: Springer Optimization and Its Applications, Book 158

General Mathematics

ISBN10: 3030429520
ISBN13: 9783030429522
Publisher: Springer
Published: Jun 24 2021
Pages: 498
Weight: 1.61
Height: 1.06 Width: 6.14 Depth: 9.21
Language: English

Two approaches are known for solving large-scale unconstrained optimization problems--the limited-memory quasi-Newton method (truncated Newton method) and the conjugate gradient method. This is the first book to detail conjugate gradient methods, showing their properties and convergence characteristics as well as their performance in solving large-scale unconstrained optimization problems and applications. Comparisons to the limited-memory and truncated Newton methods are also discussed. Topics studied in detail include: linear conjugate gradient methods, standard conjugate gradient methods, acceleration of conjugate gradient methods, hybrid, modifications of the standard scheme, memoryless BFGS preconditioned, and three-term. Other conjugate gradient methods with clustering the eigenvalues or with the minimization of the condition number of the iteration matrix, are also treated. For each method, the convergence analysis, the computational performances and thecomparisons versus other conjugate gradient methods are given.

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