Nico Strasdat

Teaching

You can find information about my teaching activities here.

Scientific Interests

• Methods for machine learning, in particular Support Vector Machines and Deep Learning
• Application of convex optimization and duality
• Newton-type methods for the solution of optimization problems
• Integer-linear programming
• Treatment of application-related optimization problems
• Effective and efficient implementation of algorithms

Short CV

• Master of Science (Mathematik) at Technische Universität Dresden (10/2012 to 09/2014)
• Bachelor of Science (Mathematik) at Technische Universität Dresden (10/2009 to 08/2012)

Publications

• Martinovic, J., Strasdat, N., de Carvalho, J. V., & Furini, F. (2022). A Combinatorial Flow-based Formulation for Temporal Bin Packing Problems. European Journal of Operational Research (Optimization Online)
• Martinovic, J., & Strasdat, N. (2022). Worst-Case Analysis of Heuristic Approaches for the Temporal Bin Packing Problem with Fire-Ups. Preprint MATH-NM-03-2022, Technische Universität Dresden (Optimization Online)
• Korbacher, L., Irnich, S., Martinovic, J., & Strasdat, N. (2022). Solving the Skiving Stock Problem by a Combination of Stabilized Column Generation and the Reflect Arc-Flow Model. Technical Report LM-2022-02, Chair of Logistics Management, Johannes Gutenberg University, Mainz
• Martinovic, J., & Strasdat, N. (2022). Theoretical Insights and a New Class of Valid Inequalities for the Temporal Bin Packing Problem with Fire-Ups. Preprint MATH-NM-01-2022, Technische Universität Dresden (Optimization Online)

• Martinovic, J., & Strasdat, N. (2022). A Heuristic-Based Reduction for the Temporal Bin Packing Problem with Fire-Ups. In International Conference on Operations Research (pp. 127-133). Springer, Cham.

• Martinovic, J., Strasdat, N., Valério de Carvalho, J., & Furini, F. (2021). Variable and constraint reduction techniques for the temporal bin packing problem with fire-ups. Optimization Letters, 1-26. (Optimization Online)

• Martinovic, J., Strasdat, N., & Selch, M. (2021). Compact integer linear programming formulations for the temporal bin packing problem with fire-ups. Computers & Operations Research, 132, 105288. (Optimization Online)

• Martinovic, J., Delorme, M., Iori, M., Scheithauer, G., & Strasdat, N. (2020). Improved flow-based formulations for the skiving stock problem. Computers & Operations Research, 113, 104770.
• Behling, R., Fischer, A., Schönefeld, K., & Strasdat, N. (2019). A special complementarity function revisited. Optimization, 68(1), 65-79.
• Fischer, A., Langensiepen, G., Luig, K., Strasdat, N., & Thies, T. (2015). Efficient optimization of hyper-parameters for least squares support vector regression. Optimization Methods and Software, 30(6), 1095-1108.
• Strasdat, N. (2014). Reduced-Set Support Vector Machines (Masterarbeit, Technische Universität Dresden)
• Strasdat, N. (2012). Random Forests und ihre Anwendung zur Regression (Bachelorarbeit, Technische Universität Dresden)

Recent and current projects

• Datengetriebene Generierung von Modellen und Sensitivitätsanalyse für Produktionsprozesse (BMBF, since 04/2020) im Verbundprojekt OptProDat
• Anspruchsvolle Freiformbeschichtung flächiger und 3-dimensionaler Substrate durch Inline-Sputtertechnick (since 08/2019)
• Newton-type Methods for Nonsmooth Equations with Nonisolated Solutions (DFG, 01/2017 to 12/2019)
• Optimierungstechniken für Klassifikation und Regression, Teil II (Industry, 01/2017 to 12/2017)
• Optimierungstechniken für Klassifikation und Regression (Industry, 10/2014 to 12/2016)
• Support-Vector-Methoden und multivariate Regression (Industry, 04/2013 to 09/2014)
• Support Vector Methoden (Industry, 09/2011 to 08/2013)