Andreas Habring

works on

convex optimization 1 gradient methods 1 optimal transport 1 primal-dual methods 1 proximal algorithms 1

From the 1 of 12 linked papers with an AI index.

most citedErgodicity of Langevin Dynamics and its Discretizations for Non-smooth Potentials

1 citations · 1 across the 5 of their papers we have counts for

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math.OC2026

Lecture Notes: Convex Optimization

Andreas Habring

These lecture notes introduce the theory and algorithms of convex optimization, covering existence results, projected subgradient descent, proximal‑gradient and accelerated gradien…

math.OC2025

On the Time Derivative of the KL Divergence for a Generalized Langevin Annealing Scheme

Andreas Habring

Consider the Langevin diffusion process $d X_{t} = \nabla lo g p_{t} (X_{t}) + 2 d W_{t}$ guided by the time-dependent probability density $p_{t} (x)$ . Let $q_{t}$ be the…

math.OC2025

Computation of a Consistent System Matrix for Cone-beam Computed Tomography

Josef Simbrunner, Clemens Krenn, Martin Zach +1

We propose a method for the computation of a consistent system matrix for two- and three-dimensional cone-beam computed tomography (CT). The method relies on the decomposition of t…

math.OC2025

Diffusion at Absolute Zero: Langevin Sampling using Successive Moreau Envelopes [journal paper]

Andreas Habring, Alexander Falk, Martin Zach +1

We propose a method for sampling from Gibbs distributions of the form $I ¨ (x) \propto exp (- U (x))$ by considering a family $(I ¨ ^{t})_{t}$ of approximations of the target density which i…

math.OC2025

Diffusion at Absolute Zero: Langevin Sampling Using Successive Moreau Envelopes [conference paper]

Andreas Habring, Alexander Falk, Thomas Pock

In this article we propose a novel method for sampling from Gibbs distributions of the form $I ¨ (x) \propto exp (- U (x))$ with a potential $U (x)$ . In particular, inspired by diffusion…