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New complexity analysis of a full Nesterov- Todd step infeasible interior-point algorithm for symmetric optimization

Behrouz Kheirfam, Nezam Mahdavi-Amiri (2013)

Kybernetika

A full Nesterov-Todd step infeasible interior-point algorithm is proposed for solving linear programming problems over symmetric cones by using the Euclidean Jordan algebra. Using a new approach, we also provide a search direction and show that the iteration bound coincides with the best known bound for infeasible interior-point methods.

Object library of algorithms for dynamic optimization problems: benchmarking SQP and nonlinear interior point methods

Jacek Błaszczyk, Andrzej Karbowski, Krzysztof Malinowski (2007)

International Journal of Applied Mathematics and Computer Science

The main purpose of this paper is to describe the design, implementation and possibilities of our object-oriented library of algorithms for dynamic optimization problems. We briefly present library classes for the formulation and manipulation of dynamic optimization problems, and give a general survey of solver classes for unconstrained and constrained optimization. We also demonstrate methods of derivative evaluation that we used, in particular automatic differentiation. Further, we briefly formulate...

On the central paths and Cauchy trajectories in semidefinite programming

Julio López, Héctor Ramírez C. (2010)

Kybernetika

In this work, we study the properties of central paths, defined with respect to a large class of penalty and barrier functions, for convex semidefinite programs. The type of programs studied here is characterized by the minimization of a smooth and convex objective function subject to a linear matrix inequality constraint. So, it is a particular case of convex programming with conic constraints. The studied class of functions consists of spectrally defined functions induced by penalty or barrier...

Optimization-based approach to path planning for closed chain robot systems

Wojciech Szynkiewicz, Jacek Błaszczyk (2011)

International Journal of Applied Mathematics and Computer Science

An application of advanced optimization techniques to solve the path planning problem for closed chain robot systems is proposed. The approach to path planning is formulated as a “quasi-dynamic” NonLinear Programming (NLP) problem with equality and inequality constraints in terms of the joint variables. The essence of the method is to find joint paths which satisfy the given constraints and minimize the proposed performance index. For numerical solution of the NLP problem, the IPOPT solver is used,...

Penalty/barrier path-following in linearly constrained optimization

Christian Grossmann (2000)

Discussiones Mathematicae, Differential Inclusions, Control and Optimization

In the present paper rather general penalty/barrier path-following methods (e.g. with p-th power penalties, logarithmic barriers, SUMT, exponential penalties) applied to linearly constrained convex optimization problems are studied. In particular, unlike in previous studies [1,11], here simultaneously different types of penalty/barrier embeddings are included. Together with the assumed 2nd order sufficient optimality conditions this required a significant change in proving the local existence of...

Primal interior point method for minimization of generalized minimax functions

Ladislav Lukšan, Ctirad Matonoha, Jan Vlček (2010)

Kybernetika

In this paper, we propose a primal interior-point method for large sparse generalized minimax optimization. After a short introduction, where the problem is stated, we introduce the basic equations of the Newton method applied to the KKT conditions and propose a primal interior-point method. (i. e. interior point method that uses explicitly computed approximations of Lagrange multipliers instead of their updates). Next we describe the basic algorithm and give more details concerning its implementation...

Primal interior-point method for large sparse minimax optimization

Ladislav Lukšan, Ctirad Matonoha, Jan Vlček (2009)

Kybernetika

In this paper, we propose a primal interior-point method for large sparse minimax optimization. After a short introduction, the complete algorithm is introduced and important implementation details are given. We prove that this algorithm is globally convergent under standard mild assumptions. Thus the large sparse nonconvex minimax optimization problems can be solved successfully. The results of extensive computational experiments given in this paper confirm efficiency and robustness of the proposed...

Recursive form of general limited memory variable metric methods

Ladislav Lukšan, Jan Vlček (2013)

Kybernetika

In this report we propose a new recursive matrix formulation of limited memory variable metric methods. This approach can be used for an arbitrary update from the Broyden class (and some other updates) and also for the approximation of both the Hessian matrix and its inverse. The new recursive formulation requires approximately 4 m n multiplications and additions per iteration, so it is comparable with other efficient limited memory variable metric methods. Numerical experiments concerning Algorithm...

Selection strategies in projection methods for convex minimization problems

Andrzej Cegielski, Robert Dylewski (2002)

Discussiones Mathematicae, Differential Inclusions, Control and Optimization

We propose new projection method for nonsmooth convex minimization problems. We present some method of subgradient selection, which is based on the so called residual selection model and is a generalization of the so called obtuse cone model. We also present numerical results for some test problems and compare these results with some other convex nonsmooth minimization methods. The numerical results show that the presented selection strategies ensure long steps and lead to an essential acceleration...

Un algoritmo de punto interior para programación cuadrática a través de problemas equivalentes separables.

Jordi Castro (1998)

Qüestiió

Se presenta un algoritmo de punto interior para la solución de problemas cuadráticos simétricos y definidos positivos, mediante su transformación en problemas equivalentes separables (esto es, la matriz de coeficientes cuadráticos es diagonal y no existen términos cruzados). El algoritmo difiere de otros ya existentes (como el implementado en el sistema LoQo) en el hecho de que soluciona las denominadas "ecuaciones normales en forma primal" (LoQo soluciona el denominado "sistema aumentado") y en...

Une procédure de purification pour les problèmes de complémentarité linéaire, monotones

Abderrahim Kadiri, Adnan Yassine (2004)

RAIRO - Operations Research - Recherche Opérationnelle

Dans cet article, nous proposons une nouvelle méthode de purification pour les problèmes de complémentarité linéaire, monotones. Cette méthode associe à chaque itéré de la suite, générée par une méthode de points intérieurs, une base non nécessairement réalisable. Nous montrons que, sous les hypothèses de complémentarité stricte et de non dégénérescence, la suite des bases converge en un nombre fini d’itérations vers une base optimale qui donne une solution exacte du problème. Le procédé adopté...

Une procédure de purification pour les problèmes de complémentarité linéaire, monotones

Abderrahim Kadiri, Adnan Yassine (2010)

RAIRO - Operations Research

Dans cet article, nous proposons une nouvelle méthode de purification pour les problèmes de complémentarité linéaire, monotones. Cette méthode associe à chaque itéré de la suite, générée par une méthode de points intérieurs, une base non nécessairement réalisable. Nous montrons que, sous les hypothèses de complémentarité stricte et de non dégénérescence, la suite des bases converge en un nombre fini d'itérations vers une base optimale qui donne une solution exacte du problème. Le procédé adopté...

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