9 documents found, page 1 of 1
Planck 2015 results XVII. Constraints on primordial non-Gaussianity
Ade, P. A. R.; Aghanim, N.; Arnaud, M.; Arrojam, F.; Ashdown, M.; Aumont, J.; Baccigalupi, C.; Ballardini, M.; Banday, A. J.; Barreiro, R. B.Made available in DSpace on 2018-11-26T17:06:52Z (GMT). No. of bitstreams: 0 Previous issue date: 2016-10-01; ESA; CNES (France); CNRS/INSU-IN2P3-INP (France); ASI (Italy); CNR (Italy); INAF (Italy); NASA (USA); DoE (USA); STFC (UK); UKSA (UK); CSIC (Spain); MINECO (Spain); JA (Spain); RES (Spain); Tekes (Finland); AoF (Finland); CSC (Finland); DLR (Germany); MPG (Germany); CSA (Canada); DTU Space (Denmark); SE...
Planck 2015 results I. Overview of products and scientific results
Adam, R.; Ade, P. A. R.; Aghanim, N.; Akrami, Y.; Alves, M. I. R.; Argueeso, F.; Arnaud, M.; Arroja, F.; Ashdown, M.; Aumont, J.; Baccigalupi, C.Made available in DSpace on 2018-11-26T17:06:58Z (GMT). No. of bitstreams: 0 Previous issue date: 2016-10-01; CNES; CNRS/INSU-IN2P3; ASI; Danish Natural Research Council; ESA; CNES (France); CNRS/INSU-IN2P3INP (France); ASI (Italy); CNR (Italy); INAF (Italy); NASA (USA); DoE (USA); STFC (UK); UKSA (UK); CSIC (Spain); MINECO (Spain); JA (Spain); RES (Spain); Tekes (Finland); AoF (Finland); CSC (Finland); DLR (Ge...
Levenberg--Marquardt Methods Based on Probabilistic Gradient Models and Inexact...
Bergou, E.; Gratton, S.; Vicente, Luís NunesThe 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 and/or the gradient model is noisy and accurate only withi...
A parallel evolution strategy for an earth imaging problem in geophysics
Diouane, Y.; Gratton, S.; Vasseur, X.; Vicente, Luís Nunes; Calandra, H.In this paper we propose a new way to compute a rough approximation solution, to be later used as a warm starting point in a more refined optimization process, 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 m...
A second-order globally convergent direct-search method and its worst-case comp...
Gratton, S.; Royer, C. W.; Vicente, Luís NunesDirect-search algorithms form one of the main classes of algorithms for smooth unconstrained derivative-free optimization, due to their simplicity and their well-established convergence results. They proceed by iteratively looking for improvement along some vectors or directions. In the presence of smoothness, first-order global convergence comes from the ability of the vectors to approximate the steepest desce...
Direct Search Based on Probabilistic Descent
Gratton, S.; Royer, C. W.; Vicente, Luís Nunes; Zhang, ZaikunDirect-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 $n$-dimensional variable space. In addition, to ensure the...
Globally convergent evolution strategies
Diouane, Y.; Gratton, S.; Vicente, Luís NunesIn 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 offspring points are computed by random generation. One relevan...
Globally convergent evolution strategies for constrained optimization
Diouane, Y.; Gratton, S.; Vicente, Luís NunesIn 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 a first approach, feasibility is first enforced by a b...
A surrogate management framework using rigorous trust-region steps
Gratton, S.; Vicente, Luís NunesSurrogate models are frequently used in the optimization engineering community as convenient approaches to deal with functions for which evaluations are expensive or noisy, or lack convexity. These methodologies do not typically guarantee any type of convergence under reasonable assumptions. In this article, we will show how to incorporate the use of surrogate models, heuristics, or any other process of attempt...