The error and perturbation bounds for the absolute value equations with some applications

To our knowledge, so far, the error and perturbation bounds for the general absolute value equations are not discussed. In order to fill in this study gap, in this paper, by introducing a class of absolute value functions, we study the error bounds and perturbation bounds for two types of absolute value equations (AVEs): Ax-B|x|=b and Ax-|Bx|=b. Some useful error bounds and perturbation bounds for the above two types of absolute value equations are presented. By applying the absolute value equations, we also obtain the error and perturbation bounds for the horizontal linear complementarity problem (HLCP). In addition, a new perturbation bound for the LCP without constraint conditions is given as well, which are weaker than the presented work in [SIAM J. Optim., 2007, 18: 1250-1265] in a way. Besides, without limiting the matrix type, some computable estimates for the above upper bounds are given, which are sharper than some existing results under certain conditions. Some numerical examples for the AVEs from the LCP are given to show the feasibility of the perturbation bounds

Complementarity and related problems

In this thesis, we present results related to complementarity problems. We study the linear complementarity problems on extended second order cones. We convert a linear complementarity problem on an extended second order cone into a mixed complementarity problem on the non-negative orthant. We present algorithms for this problem, and exemplify it by a numerical example. Following this result, we explore the stochastic version of this linear complementarity problem. Finally, we apply complementarity problems on extended second order cones in a portfolio optimisation problem. In this application, we exploit our theoretical results to find an analytical solution to a new portfolio optimisation model. We also study the spherical quasi-convexity of quadratic functions on spherically self-dual convex sets. We start this study by exploring the characterisations and conditions for the spherical positive orthant. We present several conditions characterising the spherical quasi-convexity of quadratic functions. Then we generalise the conditions to the spherical quasi-convexity on spherically self-dual convex sets. In particular, we highlight the case of spherical second order cones

Un algoritmo cuasi Newton inexacto global para problemas de complementariedad no lineal.

En este trabajo de investigación proponemos y desarrollamos un método cuasi Newton inexacto global para resolver el problema de complementariedad no lineal (PCNL) de una manera indirecta: en primer lugar, reescribiremos el PCNL como un problema de Complementariedad Horizontal (PCH) y posteriormente, reescribiremos el PCH como un problema de minimización. Cabe destacar que abordar el PCNL de esta manera nos permitirá trabajar con reformulaciones diferenciales de la versión original del problema. De igual forma, proponemos una leve modificación al algoritmo para resolver problemas de complementariedad no lineal, con el fin de obtener un método que permita encontrar las raíces positivas de sistemas de ecuaciones no lineales de gran tamaño

Morceaux Choisis en Optimisation Continue et sur les Systèmes non Lisses

MasterThis course starts with the presentation of the optimality conditions of an optimization problem described in a rather abstract manner, so that these can be useful for dealing with a large variety of problems. Next, the course describes and analyzes various advanced algorithms to solve optimization problems (nonsmooth methods, linearization methods, proximal and augmented Lagrangian methods, interior point methods) and shows how they can be used to solve a few classical optimization problems (linear optimization, convex quadratic optimization, semidefinite optimization (SDO), nonlinear optimization). Along the way, various tools from convex and nonsmooth analysis will be presented. Everything is conceptualized in finite dimension. The goal of the lectures is therefore to consolidate basic knowledge in optimization, on both theoretical and algorithmic aspects

International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book

The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions. This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more