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An inexact successive quadratic approximation method for L-1 regularized optimization
We study a Newton-like method for the minimization of an objective function $$\phi $$ϕ that is the sum of a smooth function and an $$\ell _1$$ℓ1 regularization term. This method, which is sometimesExpand
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Newton-Like Methods for Sparse Inverse Covariance Estimation
We propose two classes of second-order optimization methods for solving the sparse inverse covariance estimation problem. The first approach, which we call the Newton-LASSO method, minimizes aExpand
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  • Open Access
A family of second-order methods for convex $$\ell _1$$ℓ1-regularized optimization
This paper is concerned with the minimization of an objective that is the sum of a convex function f and an $$\ell _1$$ℓ1 regularization term. Our interest is in active-set methods that incorporateExpand
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  • Open Access
Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies
This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays inExpand
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  • Open Access
A second-order method for convex 1-regularized optimization with active-set prediction
We describe an active-set method for the minimization of an objective function φ that is the sum of a smooth convex function f and an -regularization term. A distinctive feature of the method is theExpand
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  • Open Access
An interior point method for nonlinear programming with infeasibility detection capabilities
This paper describes an interior point method for nonlinear programming endowed with infeasibility detection capabilities. The method is composed of two phases, a main phase whose goal is to seekExpand
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  • Open Access
Solving Global Optimization Problems Using MANGO
Traditional approaches for solving global optimization problems generally rely on a single algorithm. The algorithm may be hybrid or applied in parallel. Contrary to traditional approaches, thisExpand
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  • Open Access
Parallel algorithms for nonlinear optimization
Parallel algorithm design is a very active research topic in optimization as parallel computer architectures have recently become easily accessible. This thesis is about an approach for designingExpand
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  • Open Access
An alternative globalization strategy for unconstrained optimization
Abstract We propose a new globalization strategy that can be used in unconstrained optimization algorithms to support rapid convergence from remote starting points. Our approach is based on usingExpand
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  • Open Access
MANGO : A MultiAgent ENvironment for Global Optimization
Hard global optimization problems arise in many areas of engineering. However, solving these problems is a rather difficult task. Most of the existing methods work in isolation and aim at solvingExpand
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  • Open Access