# Sébastien Le Digabel

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Smart homes have the potential to achieve optimal energy consumption with appropriate scheduling. It is expected that 35% of households in North America an...

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The parallel space decomposition of the Mesh Adaptive Direct Search algorithm (PSD-MADS proposed in 2008) is an asynchronous parallel method for constrained ...

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We consider the maximum $$k$$-cut problem that involves partitioning the vertex set of a graph into $$k$$ subsets such that the sum of the weights of the...

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

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

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The classical alternating current optimal power flow problem is highly nonconvex and generally hard to solve. Convex relaxations, in particular semidefinite,...

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

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The Mesh Adaptive Direct Search algorithm (MADS) is an iterative method for constrained blackbox optimization problems. One of the optional MADS features i...

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Blackbox optimization problems are often contaminated with numerical noise, and direct search methods such as the Mesh Adaptive Direct Search (MADS) algorit...

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We present a new derivative-free trust-region (DFTR) algorithm to solve general nonlinear constrained problems with the use of an augmented Lagrangian m...

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We study derivative-free constrained optimization problems and propose a trust-region method that builds linear or quadratic models around the best feasible ...

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An augmented Lagrangian (AL) can convert a constrained optimization problem into a sequence of simpler (e.g., unconstrained) problems, which are then usual...

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We consider the maximum $$k$$-cut problem that consists in partitioning the vertex set of a graph into $$k$$ subsets such that the sum of the weights o...

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This work describes the application of a direct search method to the optimization of problems of real industrial interest, namely three new material scien...

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This document describes the NOMAD software, a C++ implementation of the Mesh Adaptive Direct Search (MADS) algorithm designed for constrained optimization of...

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The types of constraints encountered in black-box and simulation-based optimization problems differ significantly from those treated in nonlinear programmin...

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Various constrained problem formulations for the optimization of an electro-thermal wing anti-icing system in both running-wet and evaporative regimes are pr...

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The Mesh Adaptive Direct Search (MADS) algorithm is designed for blackbox optimization problems subject to general inequality constraints. Currently, MADS do...

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Constrained blackbox optimization is a difficult problem, with most approaches coming from the mathematical programming literature. The statistical literatur...

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Blackbox optimization deals with situations in which the objective function and constraints are typically computed by launching a time-consuming computer ...

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