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- Serge Gratton, Clément W. Royer, Luís Nunes Vicente, Zaikun Zhang
- SIAM Journal on Optimization
- 2015

Direct-search methods are a class of popular derivative-free algorithms characterized by evaluating the objective function using a step size and a number of (polling) directions. When applied to the minimization of smooth functions, the polling directions are typically taken from positive spanning sets which in turn must have at least n+1 vectors in an… (More)

- Serge Gratton, Luís Nunes Vicente
- SIAM Journal on Optimization
- 2014

In this paper it is proposed to equip direct-search methods with a general procedure to minimize an objective function, possibly non-smooth, without using derivatives and subject to constraints on the variables. One aims at considering constraints, most likely nonlinear or non-smooth, for which the derivatives of the corresponding functions are also… (More)

- Youssef Diouane, Serge Gratton, Luís Nunes Vicente
- Math. Program.
- 2015

In this paper we show how to modify a large class of evolution strategies (ES's) for unconstrained optimization to rigorously achieve a form of global convergence, meaning convergence to stationary points independently of the starting point. The type of ES under consideration recombines the parent points by means of a weighted sum, around which the… (More)

- Youssef Diouane, Serge Gratton, Luís Nunes Vicente
- Comp. Opt. and Appl.
- 2015

In this paper we propose, analyze, and test algorithms for constrained optimization when no use of derivatives of the objective function is made. The proposed methodology is built upon the globally convergent evolution strategies previously introduced by the authors for unconstrained optimization. Two approaches are encompassed to handle the constraints. In… (More)

Trust-region algorithms have been proved to globally converge with probability one when the accuracy of the trust-region models is imposed with a certain probability conditioning on the iteration history. In this paper, we study their complexity, providing global rates and worst case complexity bounds on the number of iterations (with overwhelmingly high… (More)

- E. Bergou, S. Gratton, L. N. Vicente
- 2014

The Levenberg-Marquardt algorithm is one of the most popular algorithms for the solution of nonlinear least squares problems. Motivated by the problem structure in data assimilation, we consider in this paper the extension of the classical Levenberg-Marquardt algorithm to the scenarios where the linearized least squares subproblems are solved inexactly… (More)

In this paper we show how to modify a large class of evolution strategies (ES) to rigorously achieve a form of global convergence, meaning convergence to stationary points independently of the starting point. The type of ES under consideration recombine the parents by means of a weighted sum, around which the offsprings are computed by random generation.… (More)

- Ali Baharev, Endre Rév, +14 authors John K. Reid
- 2009

A general framework is presented for finding all solutions to systems of nonlinear equations, or proving that there is no solution to the problem. Components of this framework are interval arithmetic, affine arithmetic, constraint propagation based on directed acyclic graph (DAG) representation of the problem, a pruning technique based on linear programming… (More)

Stellar evolutionary models simulate well binary stars when individual stellar mass and system metallicity are known. The main goal of this paper is to determine a set of stellar parameters (mass, age, helium abundance, and convection parameters) for binary systems formed by FGK main-sequence stars of Population I. For a selected group of seven binaries,… (More)

In this paper we propose a new way to compute a warm starting point for a challenging global optimization problem related to Earth imaging in geophysics. The warm start consists of a velocity model that approximately solves a full-waveform inverse problem at low frequency. Our motivation arises from the availability of massively parallel computing platforms… (More)