Zobrazeno 1 - 10
of 102
pro vyhledávání: '"Loris, Ignace"'
Autor:
Loris, Ignace, Rebegoldi, Simone
We present a numerical iterative optimization algorithm for the minimization of a cost function consisting of a linear combination of three convex terms, one of which is differentiable, a second one is prox-simple and the third one is the composition
Externí odkaz:
http://arxiv.org/abs/2311.09123
Autor:
Fest, Jean-Baptiste, Heikkilä, Tommi, Loris, Ignace, Martin, Ségolène, Ratti, Luca, Rebegoldi, Simone, Sarnighausen, Gesa
We consider a variation of the classical proximal-gradient algorithm for the iterative minimization of a cost function consisting of a sum of two terms, one smooth and the other prox-simple, and whose relative weight is determined by a penalty parame
Externí odkaz:
http://arxiv.org/abs/2212.12256
Autor:
Loris, Ignace, Rebegoldi, Simone
Publikováno v:
In Journal of Computational and Applied Mathematics 15 March 2025 457
The convergence of many proximal algorithms involving a gradient descent relies on its Lipschitz constant. To avoid computing it, backtracking rules can be used. While such a rule has already been designed for the forward-backward algorithm (FBwB), t
Externí odkaz:
http://arxiv.org/abs/2009.03576
Autor:
Chen, Jixin, Loris, Ignace
The importance of an adequate inner loop starting point (as opposed to a sufficient inner loop stopping rule) is discussed in the context of a numerical optimization algorithm consisting of nested primal-dual proximal-gradient iterations. While the n
Externí odkaz:
http://arxiv.org/abs/1806.07677
Publikováno v:
Inverse Problems 33 (2017), 055005
We consider a variable metric linesearch based proximal gradient method for the minimization of the sum of a smooth, possibly nonconvex function plus a convex, possibly nonsmooth term. We prove convergence of this iterative algorithm to a critical po
Externí odkaz:
http://arxiv.org/abs/1605.03791
Publikováno v:
SIAM Journal on Optimization 26 (2016), 891-921
We develop a new proximal-gradient method for minimizing the sum of a differentiable, possibly nonconvex, function plus a convex, possibly non differentiable, function. The key features of the proposed method are the definition of a suitable descent
Externí odkaz:
http://arxiv.org/abs/1506.00385
Autor:
Nassiri, Vahid, Loris, Ignace
Quantile regression is studied in combination with a penalty which promotes structured (or group) sparsity. A mixed $\ell_{1,\infty}$-norm on the parameter vector is used to impose structured sparsity on the traditional quantile regression problem. A
Externí odkaz:
http://arxiv.org/abs/1302.6088
Autor:
Loris, Ignace, Verhoeven, Caroline
A qualitative comparison of total variation like penalties (total variation, Huber variant of total variation, total generalized variation, ...) is made in the context of global seismic tomography. Both penalized and constrained formulations of seism
Externí odkaz:
http://arxiv.org/abs/1203.4451
Autor:
Loris, Ignace, Verhoeven, Caroline
We propose an iterative algorithm for the minimization of a $\ell_1$-norm penalized least squares functional, under additional linear constraints. The algorithm is fully explicit: it uses only matrix multiplications with the three matrices present in
Externí odkaz:
http://arxiv.org/abs/1202.3362