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Andreas Themelis

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Associate professor

Kyushu University

Japan

Research Interests

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About

Andreas Themelis is an Associate Professor at Kyushu University, Japan. His research interests include convex optimization, nonconvex optimization, and the development of proximal algorithms. Recent publications highlight his work on adaptive algorithms, optimal grid layouts for offshore assets, and advanced optimization techniques under local Lipschitz conditions.

Recent Grants

Grant: Open

Adaptive optimization: parameter-free self-tuning algorithms beyond smoothness and convexity

Open Date: 2024-04-01

Close Date: 2027-03-01

Grant: Close

New-generation optimization algorithms for engineering

Open Date: 2021-04-01

Close Date: 2024-03-31

Grant: Close

Proximal Algorithms for Structured Nonconvex Optimization

Open Date: 2016-09-08

Close Date: 2018-12-12

Articles (11)

Year:

2026

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2026

Year:

2025

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2023

Year:

2023

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2022

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2022

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2022

Year:

2022

Year:

2021

Year:

2020

Collaborators (3)

Puya Latafat

Assistant Professor

IMT School for Advanced Studies Lucca

ITALY

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Dirk Van Hertem

Professor

Katholieke Universiteit Leuven

BELGIUM

View details

Panagiotis Patrinos

Professor

KU Leuven

BELGIUM

View details

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Andreas Themelis

Research Interests

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About

Recent Grants

Articles (11)

Linesearch-Free Adaptive Bregman Proximal Gradient for Convex Minimization Under Local Relative Smoothness

Bregman Level Proximal Subdifferentials and New Characterizations of Bregman Proximal Operators

Adaptive proximal algorithms for convex optimization under local Lipschitz continuity of the gradient

Optimal Grid Layouts for Hybrid Offshore Assets in the North Sea under Different Market Designs

Dualities for non-Euclidean smoothness and strong convexity under the light of generalized conjugacy

Bregman Finito/MISO for Nonconvex Regularized Finite Sum Minimization without Lipschitz Gradient Continuity

Douglas–Rachford splitting and ADMM for nonconvex optimization: accelerated and Newton-type linesearch algorithms

Proximal Gradient Algorithms Under Local Lipschitz Gradient Continuity

Block-coordinate and incremental aggregated proximal gradient methods for nonsmooth nonconvex problems

A Bregman Forward-Backward Linesearch Algorithm for Nonconvex Composite Optimization: Superlinear Convergence to Nonisolated Local Minima

Douglas--Rachford Splitting and ADMM for Nonconvex Optimization: Tight Convergence Results

Collaborators (3)

Puya Latafat

Dirk Van Hertem

Panagiotis Patrinos

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