Themelis Andreas

Affiliation and department：
Research theme for competitive and other funds (2)：

2021 - 2023 The project is concerned with optimization algorithms for engineering, in compliance with the application challenges: efficiency, limited power of microprocessors, and nonconvexity of the problems. The final goal is to provide efficient open-source multi-purpose software with theoretical guarantees.
2021 - 2021 The main objective of the project is to develop learning-based techniques for devising ad-hoc tuning-free optimization algorithms for convex and nonconvex optimization problems. A novel universal framework will be developed, which will serve as a solid theoretical ground for the development of new learning paradigms to train optimization methods subject to certificates of (speed of) conver-gence and quality of output solution.

Papers (13)：

Stephen Hardy, Andreas Themelis, Kaoru Yamamoto, Hakan Ergun, Dirk Van Hertem. Optimal Grid Layouts for Hybrid Offshore Assets in the North Sea under Different Market Designs. IEEE Transactions on Energy Markets, Policy, and Regulation. 2023
Hermans, Ben, Themelis, Andreas, Patrinos, Panagiotis. QPALM: A proximal augmented Lagrangian method for nonconvex quadratic programs. Mathematical Programming Computation. 2022
Themelis, Andreas, Stella, Lorenzo, Patrinos, Panagiotis. Douglas-Rachford splitting and ADMM for nonconvex optimization: Accelerated and Newton-type algorithms. Computational Optimization and Applications. 2022. 82
De~Marchi, Alberto, Themelis, Andreas. Proximal Gradient Algorithms under Local Lipschitz Gradient Continuity: A Convergence and Robustness Analysis of PANOC. Journal of Optimization Theory and Applications. 2022. 194
Latafat, Puya, Themelis, Andreas, Patrinos, Panagiotis. Block-coordinate and incremental aggregated proximal gradient methods for nonsmooth nonconvex problems. Mathematical Programming. 2021

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MISC (5)：

Andreas Themelis. Flock navigation with dynamic hierarchy and subjective weights using nonlinear MPC. 2022
Andreas Themelis. An Interior Proximal Gradient Method for Nonconvex Optimization. 2022
Andreas Themelis. SPIRAL: A Superlinearly Convergent Incremental Proximal Algorithm for Nonconvex Finite Sum Minimization. 2022
Andreas Themelis. Conjugate dualities for relative smoothness and strong convexity under the light of generalized convexity. 2021
Andreas Themelis. Bregman Finito/MISO for nonconvex regularized finite sum minimization without Lipschitz gradient continuity. 2021

Lectures and oral presentations (9)：

A universal majorization-minimization framework for the convergence analysis of nonconvex proximal algorithms
(6th International Conference on Continuous Optimization 2019)
Proximal envelopes
(17th IEEE European Control Conference 2018)
A simple and efficient algorithm for Nonlinear MPC
(56th IEEE Conference on Decision and Control 2017)
Accelerated Douglas-Rachford splitting and ADMM for structured nonconvex optimization
(CMO-BIRS Workshop on Splitting Algorithms, Modern Operator Theory and Applications 2017)
Newton-type proximal algorithms for nonconvex optimization
(LCCC focus period on large scale and distributed optimization 2017)

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