Dominique Orban
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87 results — page 2 of 5
We develop a Levenberg-Marquardt method for minimizing the sum of a smooth nonlinear least-squares term f(x)=12‖
and a nonsmoo...
This paper presents PDENLPModels.jl a new Julia package for modeling and discretizing optimization problems with mixed algebraic and partial differential equ...
BibTeX referenceOn GSOR, the generalized successive overrelaxation method for double saddle-point problems
We consider the generalized successive overrelaxation (GSOR) method for solving a class of block three-by-three saddle-point problems. Based on the necessary...
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We consider the problem of training a deep neural network with nonsmooth regularization to retrieve a sparse and efficient sub-structure. Our regularizer is ...
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The conjugate gradient (CG) method is a classic Krylov subspace method for solving symmetric positive definite linear systems. We introduce an analogous sem...
BibTeX referenceComputing a sparse projection into a box
We describe a procedure to compute a projection of w \in ℝ^n
into the intersection of the so-called zero-norm ball k B_0
of radius k
, i....
DCISolver.jl: A Julia solver for nonlinear optimization using dynamic control of infeasibility
This paper presents DCISolver.jl a new Julia package implementating the Dynamic Control of Infeasibility method (DCI), introduced by Bielschowsky & Gomes (20...
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We introduce an iterative method named GPMR for solving 2X2 block unsymmetric linear systems. GPMR is based on a new process that reduces simultaneously...
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In this paper, we consider both first- and second-order techniques to address continuous optimization problems arising in machine learning. In the first-orde...
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We introduce iterative methods named TriCG and TriMR for solving symmetric quasi-definite systems based on the orthogonal tridiagonalization process proposed...
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Algorithm NCL is designed for general smooth optimization problems
where first and second derivatives are available,
including problems whose constrai...
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We propose a new stochastic variance-reduced damped L-BFGS algorithm, where we leverage estimates of bounds on the largest and smallest eigenvalues of the He...
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We present a modeling of bundle adjustment problems in Julia, as well as a solver for non-linear least square problems (including bundle adjustment problems)...
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We consider the iterative solution of regularized saddle-point systems. When the leading block is symmetric and positive semi-definite on an appropriate sub...
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We provide eigenvalues bounds for a new formulation of the step equations in interior methods for convex quadratic optimization. The matrix of our formulati...
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Artificial Intelligence (AI) is the next society transformation builder. Massive AI-based applications include cloud servers, cell phones, cars, and pandemic...
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We introduce an iterative method named BiLQ for solving general square linear systems Ax=b
based on the Lanczos biorthogonalization process defined by ...
In this paper, we compare the BFGS and the conjugate gradient (CG) methods for solving unconstrained problems with a trust-region algorithm. The main result ...
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Statistical image reconstruction in X-Ray computed tomography yields large-scale regularized linear least-squares problems with nonnegativity bounds, where t...
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We build upon Estrin et al. (2019) to develop a general constrained nonlinear optimization algorithm based on a smooth penalty function proposed by Fletch...
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