Programa de Doutorado: Métodos Computacionais de Otimização - Aula 01

Professor: Alfredo Iusem Previous classes: https://bit.ly/2ECXNZs One-dimensional optimization methods. Methods for unconstrained optimization (linear descent and search methods, gradient method, Newton's method, quasi-Newton methods, conjugate direction methods). Convergence globalization strategies. Methods for constrained optimization (projected gradient methods, feasible direction methods, external penalty, internal penalty, augmented Lagrangians, sequential quadratic programming). Methods for non-differentiable optimization (subgradient methods, shear plane method, beam methods). References: BERTSEKAS, DP – Nonlinear Programming. Athena Scientific, 1995. BONNANS, J.F., GILBERT J-CH., LEMARÉCHAL, C., SAGASTIZÁBAL, C. – Numerical optimization: theoretical and practical aspects. 2nd ed., Berlin; New York. Springer, 2006. DENNIS JR, J. E., SCHNABEL, R. B. – Numerical methods for unconstrained optimization and nonlinear equations. Corrected reprint of the 1983 original. Classics in Applied Mathematics, 16. Society of Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 1996. IZMAILOV, A. E., SOLODOV, M. – Optimization, volume 2. Rio de Janeiro, IMPA, 2007. IMPA Social Networks: https://linktr.ee/impabr IMPA - Institute of Pure and Applied Mathematics © https://impa.br | http://impa.br/videos The rights to all material on this channel belong to the Institute of Pure and Applied Mathematics. The total or partial use of the content is prohibited without prior written authorization from said owner, except in cases provided for by current legislation. The rights over all the material in this channel belong to the Institute of Pure and Applied Mathematics, and it is forbidden to use all or part of it without prior written authorization from the aforementioned owner, except in the cases prescribed by current legislation.