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A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization

Moêz Soltani, Abdelkader Chaari, Fayçal Ben Hmida (2012)

International Journal of Applied Mathematics and Computer Science

This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Then, particle swarm optimization is employed to finally tune parameters of the obtained...

A novel kernel function bridging iteration bounds in interior-point algorithms for linear programming

Imene Touil, Sajad Fathi-Hafshejani (2025)

Kybernetika

Kernel functions play an important role in designing and analyzing interior-point methods. They are not only used for determining search directions but also for measuring the distance between the given iterate and the μ -center in the algorithms. Currently, interior-point methods based on kernel functions are among the most effective methods for solving different types of optimization problems and are very active research area in mathematical programming. Therefore, in this work, we introduce a novel...

A numerical feasible interior point method for linear semidefinite programs

Djamel Benterki, Jean-Pierre Crouzeix, Bachir Merikhi (2007)

RAIRO - Operations Research

This paper presents a feasible primal algorithm for linear semidefinite programming. The algorithm starts with a strictly feasible solution, but in case where no such a solution is known, an application of the algorithm to an associate problem allows to obtain one. Finally, we present some numerical experiments which show that the algorithm works properly.

A numerically stable least squares solution to the quadratic programming problem

E. Übi (2008)

Open Mathematics

The strictly convex quadratic programming problem is transformed to the least distance problem - finding the solution of minimum norm to the system of linear inequalities. This problem is equivalent to the linear least squares problem on the positive orthant. It is solved using orthogonal transformations, which are memorized as products. Like in the revised simplex method, an auxiliary matrix is used for computations. Compared to the modified-simplex type methods, the presented dual algorithm QPLS...

A penalty ADMM with quantized communication for distributed optimization over multi-agent systems

Chenyang Liu, Xiaohua Dou, Yuan Fan, Songsong Cheng (2023)

Kybernetika

In this paper, we design a distributed penalty ADMM algorithm with quantized communication to solve distributed convex optimization problems over multi-agent systems. Firstly, we introduce a quantization scheme that reduces the bandwidth limitation of multi-agent systems without requiring an encoder or decoder, unlike existing quantized algorithms. This scheme also minimizes the computation burden. Moreover, with the aid of the quantization design, we propose a quantized penalty ADMM to obtain the...

A penalty approach for a box constrained variational inequality problem

Zahira Kebaili, Djamel Benterki (2018)

Applications of Mathematics

We propose a penalty approach for a box constrained variational inequality problem ( BVIP ) . This problem is replaced by a sequence of nonlinear equations containing a penalty term. We show that if the penalty parameter tends to infinity, the solution of this sequence converges to that of BVIP when the function F involved is continuous and strongly monotone and the box C contains the origin. We develop the algorithmic aspect with theoretical arguments properly established. The numerical results tested on...

A perturbation approach to approximate value iteration for average cost Markov decision processes with Borel spaces and bounded costs

Óscar Vega-Amaya, Joaquín López-Borbón (2019)

Kybernetika

The present paper studies the approximate value iteration (AVI) algorithm for the average cost criterion with bounded costs and Borel spaces. It is shown the convergence of the algorithm and provided a performance bound assuming that the model satisfies a standard continuity-compactness assumption and a uniform ergodicity condition. This is done for the class of approximation procedures that can be represented by linear positive operators which give exact representation of constant functions and...

A polarized adaptive schedule generation scheme for the resource-constrained project scheduling problem

Reza Zamani (2012)

RAIRO - Operations Research

This paper presents a hybrid schedule generation scheme for solving the resource-constrained project scheduling problem. The scheme, which is called the Polarized Adaptive Scheduling Scheme (PASS), can operate in a spectrum between two poles, namely the parallel and serial schedule generation schemes. A polarizer parameter in the range between zero and one indicates how similarly the PASS behaves like each of its two poles. The presented hybrid is...

A polarized adaptive schedule generation scheme for the resource-constrained project scheduling problem

Reza Zamani (2012)

RAIRO - Operations Research

This paper presents a hybrid schedule generation scheme for solving the resource-constrained project scheduling problem. The scheme, which is called the Polarized Adaptive Scheduling Scheme (PASS), can operate in a spectrum between two poles, namely the parallel and serial schedule generation schemes. A polarizer parameter in the range between zero and one indicates how similarly the PASS behaves like each of its two poles. The presented hybrid is...

A polyhedral study of a two level facility location model

Mourad Baïou, Francisco Barahona (2014)

RAIRO - Operations Research - Recherche Opérationnelle

We study an uncapacitated facility location model where customers are served by facilities of level one, then each level one facility that is opened must be assigned to an opened facility of level two. We identify a polynomially solvable case, and study some valid inequalities and facets of the associated polytope.

A polynomial algorithm for minDSC on a subclass of series Parallel graphs

Salim Achouri, Timothée Bossart, Alix Munier-Kordon (2009)

RAIRO - Operations Research

The aim of this paper is to show a polynomial algorithm for the problem minimum directed sumcut for a class of series parallel digraphs. The method uses the recursive structure of parallel compositions in order to define a dominating set of orders. Then, the optimal order is easily reached by minimizing the directed sumcut. It is also shown that this approach cannot be applied in two more general classes of series parallel digraphs.

A Polynomial-time Interior-point Algorithm for Convex Quadratic Semidefinite Optimization

Y. Q. Bai, F. Y. Wang, X. W. Luo (2010)

RAIRO - Operations Research

In this paper we propose a primal-dual interior-point algorithm for convex quadratic semidefinite optimization problem. The search direction of algorithm is defined in terms of a matrix function and the iteration is generated by full-Newton step. Furthermore, we derive the iteration bound for the algorithm with small-update method, namely, O( n log n ε ), which is best-known bound so far.

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