The mesh adaptive direct search (MADS) algorithm is designed for blackbox optimization problems for which the functions defining the objective and the constr...

We propose an infeasible interior-point algorithm for constrained linear least-squares problems based on the primal-dual regularization of convex program...

We investigate surrogate-assisted strategies for global derivative-free optimization using the mesh adaptive direct search MADS blackbox optimization algorit...

We propose a factorization-free method for equality-constrained optimization based on a problem in which all constraints are systematically regularized. ...

For optimization problems involving many nonlinear inequality constraints, we extend the bound-constrained (BCL) and linearly-constrained (LCL) augmented-La...

Despite the lack of theoretical and practical convergence support, the Nelder-Mead (NM) algorithm is widely used to solve unconstrained optimization proble...

We consider the solution of derivative-free optimization problems with continuous, integer, discrete and categorical variables in the context of costly black...

Global optimization problems are very hard to solve, especially when the nonlinear constraints are highly nonconvex, which can result in a large number of di...

We study X-ray tomograqphic reconstruction using statistical methods. The problem is expressed in cylindrical coordinates, which yield significant computatio...

The particularities of the aircraft parts riveting process simulation necessitate the solution of a large amount of contact problems. We propose a primal-dua...

Nanoparticle-based drug delivery is a promising method to increase the therapeutic index of anti-cancer agents with low median toxic dose. The delivery effic...

The problem of output stabilization is studied for a class of linear hybrid systems subject to signal uncertainties: linear impulsive systems under dwell-t...

This paper proposes a hierarchical decision making model for a coupled planning and operation problem of an advanced microgrid. The proposed model, is formul...

In this article we consider a difficult combinatorial optimization problem arising from the operation of a system for testing electronic circuit boards (EC...

We propose an iterative method named LSLQ for solving linear least-squares problems \(A x \approx b\) of any shape. The method is based on the Golub and K...

For pattern-based simulation methods such as SIMPAT, filtersim, wavesim, ect, patterns are stored by scanning a training image with a sliding template. Dimen...

Locally weighted regression combines the advantages of polynomial regression and kernel smoothing. We present three ideas for appropriate and effective use...

The \(k\)-means is a benchmark algorithm used in cluster analysis. It belongs to the large category of heuristics based on location-allocation steps that ...

The balanced clustering problem consists of partitioning a set of \(n\) objects into \(K\) equal-sized clusters as long as \(n\) is a multiple of `(K...

For positive definite linear systems (or semidefinite consistent systems), we use Gauss-Radau quadrature to obtain a cheaply computable upper bound on the ...