Nonlinear Conjugate Gradient Methods for Unconstrained Optimization

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).

“The book is well written for understanding several kinds of nonlinear CG methods and theirconvergence 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)

• 1. Introduction.- 2. Linear Conjugate Gradient Algorithm.- 3. General Convergence Results for Nonlinear Conjugate Gradient Methods.- 4.  Standard Conjugate Gradient Methods.- 5. Acceleration of Conjugate Gradient Algorithms.- 6. Hybrid and Parameterized Conjugate Gradient Methods.- 7. Conjugate Gradient Methods as Modifications of the Standard Schemes.- 8. Conjugate Gradient Methods Memoryless BFGS Preconditioned.- 9. Three-Term Conjugate Gradient Methods.- 10. Other Conjugate Gradient Methods.- 11. Discussion and Conclusions.- References.- Author Index.- Subject Index.