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math.OC2026

Primal-dual multigrid methods for nonsmooth optimization

Felipe Guerra, Tuomo Valkonen

In optimization, one often encounters problems of the form . In this work, we combine primal-dual algorithms with multigrid techniques for their solution. T…

math.OC2026

Single-loop approaches to nonsmooth bilevel optimisation

Ensio Suonperä, Tuomo Valkonen

We study bilevel optimisation problems in which the inner problem is represented as a set-valued, parametric constraint. We develop relevant optimistic and pessimistic calculus rul…

math.OC2026

Dynamic inverse problems: Single-loop online algorithms

Jyrki Jauhiainen, Yassine Nabou, Tuomo Valkonen

We study efficient online methods for dynamic inverse problems with infinite time horizon. We concentrate, in particular, on problems whose forward model arises from a PDE. Our mot…

math.OC20265 cited

Introduction to Nonsmooth Analysis and Optimization

Christian Clason, Tuomo Valkonen

Functions that are not differentiable in the classical sense have become a central tool in modern mathematical models for imaging, inverse problems, machine learning, and optimal c…

math.OC2026

Forward-backward splitting in bilaterally bounded Alexandrov spaces

Heikki von Koch, Tuomo Valkonen

With the goal of solving optimisation problems on non-Riemannian manifolds, such as geometrical surfaces with sharp edges, we develop and prove the convergence of a forward-backwar…

math.OC2026

Differential estimates for fast first-order multilevel nonconvex optimisation

Neil Dizon, Tuomo Valkonen

With a view on bilevel and PDE-constrained optimisation, we develop iterative estimates of for composite functions , where is the…