29 citations · 36 across the 2 of their papers we have counts for
7 papers · 1 filter
Regularisation, optimisation, subregularity
Tuomo Valkonen
Regularisation theory in Banach spaces, and non--norm-squared regularisation even in finite dimensions, generally relies upon Bregman divergences to replace norm convergence. This…
Relaxed Gauss-Newton methods with applications to electrical impedance tomography
Jyrki Jauhiainen, Petri Kuusela, Aku Seppänen +1
As second-order methods, Gauss--Newton-type methods can be more effective than first-order methods for the solution of nonsmooth optimization problems with expensive-to-evaluate sm…
Predictive online optimisation with applications to optical flow
Tuomo Valkonen
Online optimisation revolves around new data being introduced into a problem while it is still being solved; think of deep learning as more training samples become available. We ad…
Primal-dual block-proximal splitting for a class of non-convex problems
Stanislav Mazurenko, Jyrki Jauhiainen, Tuomo Valkonen
We develop block structure adapted primal-dual algorithms for non-convex non-smooth optimisation problems whose objectives can be written as compositions of non-smoo…
Inertial, corrected, primal-dual proximal splitting
Tuomo Valkonen
We study inertial versions of primal-dual proximal splitting, also known as the Chambolle--Pock method. Our starting point is the preconditioned proximal point formulation of this…
Acceleration and global convergence of a first-order primal--dual method for nonconvex problems
Christian Clason, Stanislav Mazurenko, Tuomo Valkonen
The primal--dual hybrid gradient method (PDHGM, also known as the Chambolle--Pock method) has proved very successful for convex optimization problems involving linear operators ari…