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Supervised theses


Supervised bachelor theses
  • Oksana Slavova: Numerische Verfahren der Optimierung (Bachelor seminar, U Vienna, 2022)
  • Nikolaus Hauschka: Fortsetzungssätze für Lipschitz-stetige Funktionen (Bachelor seminar, U Vienna, 2021)
  • Chiara Schindler: Einige Charakterisierungen der Reflexivität von Banachräumen (Bachelor seminar, U Vienna, 2021)
  • Marcel Harrer: The Projected Gradient Method (Bachelor seminar, U Vienna, 2020)
  • Rossen Nenov: Das globalisierte Newton-Verfahren (Bachelor seminar, U Vienna, 2020)
  • Philip Neuhart: Linear Learning Machines (Bachelor seminar, U Vienna, 2020)
  • Lisa Krögner: Standortoptimierung (Bachelor of Education seminar, U Vienna, 2020)
  • Xandro Moritz Bayer: Das Gradientenverfahren (Bachelor seminar, U Vienna, 2017)
  • Andrea Kammerhofer: Die Subgradientenmethode (Bachelor seminar 2, U Vienna, 2016)
  • Andrea Kammerhofer: Transportoptimierung - Fragestellungen und Realisierung in Matlab (Bachelor seminar 1, U Vienna, 2016)
  • Klaus Kastner: Support-Vektor-Maschinen (Bachelor seminar 2, U Vienna, 2015)
  • Eva Hornakova: Zur Konvergenz des Gradientenverfahrens mit Inertialeffekten (Bachelor seminar 2, U Vienna, 2015)
  • Petra Fiala: Gradient Method versus Fast Gradient Method (Bachelor seminar 1, U Vienna, 2015)
  • Marlene Lepuschitz: Duale Glättungsverfahren in der Bildverarbeitung (Bachelor seminar 2, U Vienna, 2015)
  • Marlene Lepuschitz: Beschleunigung des ISTA Algorithmus mit Anwendungen in der Bildverarbeitung (Bachelor seminar 1, U Vienna, 2014)

Supervised diploma theses
  • Felisa Weidner: Zur Anwendbarkeit von Splitting- und Glättungsverfahren bei Aufgaben des maschinellen Lernens (Chemnitz UT, 2013)
  • Sebastian Banert: A Relaxed Forward-Backward Splitting Algorithm for Inclusions of Sums of Monotone Operators (Chemnitz UT, 2012)
  • Albrecht Eckardt: Proximal Splitting Methods for Solving Nondifferentiable Convex Optimization Problems (Chemnitz UT, 2012)
  • Christopher Hendrich: A Double Smoothing Technique for Solving Nondifferentiable Convex Optimization Problems (Chemnitz UT, 2012)
  • Juliana Stoye: Untersuchung des Einflusses von Kombinationen von Fertigungs- und Montageparametern auf die Prüfparameter der Piezo-Pumpe-Düse (Chemnitz UT, 2007)
  • Jan Winkler: Konjugierte Dualität bei zusammengesetzten konvexen Funktionen in reellen linearen Räumen (Chemnitz UT, 2007)
  • André Heinrich: Optimierungsstrategien zum automatischen Parametrieren von Motorsteuergeräten (Chemnitz UT, 2006)
  • Susann Rösch: Erweiterung des Mond-Weir-Dualitätskonzepts für konvexe Optimierungsaufgaben (Chemnitz UT, 2006)
  • Sorin-Mihai Grad: Multiobjective Duality for Convex Semidefinite Programming Problems (BBU Cluj-Napoca, 2001)

Supervised master theses
  • Tuan Minh Le: Convergence Rates of Extragradient Type Methods for Solving Smooth Convex-Concave Problems“ (U Vienna, 2024)
  • Marcel Harrer: A Detailed Look at the Adaptive Gradient Descent Method (U Vienna, 2023)
  • Chiara Schindler: Continuous Time Approach of a Perturbed Second Order Dynamical System Combining a Vanishing Damping Term with a Hessian-analogue Damping Term (U Vienna, 2023)
  • Philip Neuhart: Adversarial Training with Lookahead (U Vienna, 2022)
  • Rossen Nenov: Convergence Rates of the Nesterov Accelerated Gradient Method under Geometric Conditions and Time Scaling (U Vienna, 2022)
  • Konstantin Sonntag: Optimization of Remedial Actions in the European Electrical Transmission System using Interior-Point Methods (U Vienna, 2021)
  • Valentyn Boreiko: Second-Order Stochastic Non-Convex Optimization (U Vienna, 2020, co-supervisor: Dan Alistarh, IST Austria)
  • Michael Sedlmayer: Analysis and Stochastic Programming in Economics of Wind Energy (U Vienna, 2018)
  • Klaus Kastner: Stochastic Primal-Dual Forward-Backward-Forward Algorithm with Applications in Machine Learning (U Vienna, 2018)
  • Markus Gumpinger: Kernel Methods for Machine Learning (U Vienna, 2017)
  • Jacques Veloso Dias: Inertial Krasnosel'skii-Mann Algorithms (U Vienna, 2017)
  • Felix Heidinger: Mathematical Modelling and Automatic Optimization of the Powertrain and the Transmission Control (U Vienna, 2017)

(Co-) Supervised dissertations
  • Michael Sedlmayer: Convergence Rate Analysis of Optimisation and Minimax Algorithms for Machine Learning (U Vienna, 2023)
  • Dang-Khoa Nguyen: Numerical Algorithms for Structured Nonsmooth and Nonconvex Optimization Problems (U Vienna, 2021)
  • Axel Böhm: Quantitative Convergence Estimates of Deterministic and Stochastic Methods for Optimization and Minimax Problems (U Vienna, 2020)
  • Dennis Meier: Improving the Convergence Behaviour of Splitting Algorithms for Monotone Inclusions in Hilbert Spaces: From Weak to Strong Convergence (U Vienna, 2020)
  • Sebastian Banert: Splitting Algorithms in Hilbert spaces and Beyond (U Vienna, 2017)
  • Christopher Hendrich: Proximal Splitting Methods in Nonsmooth Convex Optimization (Chemnitz UT, 2014)
  • Erika Nagy: Numerical Methods for Approximating Zeros of Operators and for Solving Variational Inequalities with Applications (BBU Cluj-Napoca, 2013)
  • Alina Ramona Frătean: Dual Representations and Subdifferential Formulae for Convex Risk Functions. Contributions to Extension Theorems for Set-Valued Maps (BBU Cluj-Napoca, 2013)
  • André Heinrich: Fenchel Duality-Based Algorithms for Convex Optimization Problems with Applications in Machine Learning and Image Restoration (Chemnitz UT, 2013)
  • Nicole Lorenz: Application of the Duality Theory. New Possibilities within the Theory of Risk Measures, Portfolio Optimization and Machine Learning (Chemnitz UT, 2012)
  • Delia-Maria Nechita: On the Dini-Hadamard Subdifferential Calculus in Banach Spaces (BBU Cluj-Napoca, 2012)
  • Szilárd Csaba László: The Theory of Monotone Operators with Applications (BBU Cluj-Napoca, 2012)
  • Anca Grad: Improved Optimality Conditions for Scalar, Vector and Set-Valued Optimization Problems (BBU Cluj-Napoca, 2010)
  • Ernö Robert Csetnek: Overcoming the Failure of the Classical Generalized Interior-Point Regularity Conditions in Convex Optimization. Applications of the Duality Theory to Enlargements of Maximal Monotone Operators (Chemnitz UT, 2009)
  • Ioan Bogdan Hodrea: Farkas-Type Results for Convex and Non-Convex Inequality Systems (Chemnitz UT, 2007)
  • Lkhamsuren Altangerel: A Duality Approach to Gap Functions for Variational Inequalities and Equilibrium Problems (Chemnitz UT, 2006)
  • Sorin-Mihai Grad: New Insights into Conjugate Duality (Chemnitz UT, 2006)
  • Emese Tünde Vargyas: Duality for Convex Composed Programming Problems (Chemnitz UT, 2004)
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