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Found 8 papers, 4 papers with code
Convergent plug-and-play with proximal denoiser and unconstrained regularization parameter
The stepsize condition for nonconvex convergence of the proximal algorithm in use then translates to restrictive conditions on the regularization parameter of the inverse problem.
Product of Gaussian Mixture Diffusion Models
In this work we tackle the problem of estimating the density $ f_X $ of a random variable $ X $ by successive smoothing, such that the smoothed random variable $ Y $ fulfills the diffusion partial differential equation $ (\partial_t - \Delta_1)f_Y(\,\cdot\,, t) = 0 $ with initial condition $ f_Y(\,\cdot\,, 0) = f_X $.
Explicit Diffusion of Gaussian Mixture Model Based Image Priors
In this work we tackle the problem of estimating the density $f_X$ of a random variable $X$ by successive smoothing, such that the smoothed random variable $Y$ fulfills $(\partial_t - \Delta_1)f_Y(\,\cdot\,, t) = 0$, $f_Y(\,\cdot\,, 0) = f_X$.
A relaxed proximal gradient descent algorithm for convergent plug-and-play with proximal denoiser
This paper presents a new convergent Plug-and-Play (PnP) algorithm.
"FISTA" in Banach spaces with adaptive discretisations
FISTA is a popular convex optimisation algorithm which is known to converge at an optimal rate whenever a minimiser is contained in a suitable Hilbert space.
Optimization and Control
Mumford-Shah functionals on graphs and their asymptotics
We consider adaptations of the Mumford-Shah functional to graphs.
Robust supervised classification and feature selection using a primal-dual method
This paper deals with supervised classification and feature selection in high dimensional space.
Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications
We propose a stochastic extension of the primal-dual hybrid gradient algorithm studied by Chambolle and Pock in 2011 to solve saddle point problems that are separable in the dual variable.
