# Lectures, seminars and dissertations

* Dates within the next 7 days are marked by a star.

Prof. Dr. Stefan Ruzika (RPTU)

**Multiobjective Optimization An Introduction and Some Current Research Topics**

*** ** Monday 12 August 2024, 15:15, Y225a

Multiobjective optimization is about making decisions while considering multiple conflicting objective functions. This field of research is theoretically fascinating and practically extremely useful. In this presentation, we give a brief and understandable introduction to multiobjective optimization and present a few important results. In particular, we will survey some notions of optimality, discuss the relevance of scalarization methods, and present the concept of approximation algorithms for multiobjective optimization problems.

Prof. Bruno Fanzeres (Pontifical Catholic University of Rio de Janeiro)

**Task-Based Prescriptive Trees for Two-Stage Linear Decision-Making Problems: Reformulations, Heuristic Strategies, and Applications**

*** ** Tuesday 13 August 2024, 15:15, M1 (M232)

Most decision-making under uncertainty problems found in industry and studied by the scientific community can be framed as a two-stage stochastic program. In the past decades, the standard framework to address this class of mathematical programming problems follows a sequential two-step process, usually referred to as estimate-then-optimize, in which a predictive distribution of the uncertain parameters is firstly estimated, based on some machine/statistical learning (M/SL) method, and, then, a decision is prescribed by solving the two-stage stochastic program using the estimated distribution. In this context, most M/SL methods typically focus only on minimizing the prediction error of the uncertain parameters, not accounting for its impact on the downstream decision problem. However, practitioners argue that their main interest is to obtain near-optimal solutions from the available data with minimum decision error rather than a least-error prediction. Therefore, in this talk, we discuss the new framework referred to as task-based learning in which the M/SL training function also accounts for the downstream decision problem. As the M/SL method, we focus on decision trees, and study decision-making problems framed as a two-stage linear program. We present an exact Mixed-Integer Linear Program (MILP) formulation for the task-based learning method and construct two efficient recursive-partitioning Heuristic Strategies for the MILP. We conclude the talk by analyzing a set of numerical experiments illustrating the capability and effectiveness of the task-based prescriptive tree learning framework, benchmarking against the standard estimate-then-optimize framework, and discussing the computational capability of the constructed heuristic strategies vis-à-vis the MILP formulation.

Armaan Hooda (TUKOKE competition awardee)

**Exploring time-dependent carrying capacity in the logistic population growth model**

Tuesday 20 August 2024, 10:15, M3 (M234)

Jinwoo Sung (Chicago)

**TBA**

Tuesday 10 September 2024, 10:15, M3 (M234)

Mathematical Physics

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