• Calendar

January 12, Tuesday
12:00 – 13:30

In experimental optimization the quality of candidate solutions can be evaluated only by means of an experiment in the real-world. These experiments are often time-consuming and/or expensive, and are typically limited to several dozens or hundreds of trials. High-dimensional problems (i.e., at least 80 search variables) cannot be efficiently handled by classical convex optimizers, and thus require an alternative treatment. Derandomized Evolution Strategies (DES) are powerful bio-inspired search methods, originating from Evolutionary Algorithms, that incorporate statistical learning for efficient derandomized search. This talk will focus on the theory behind state-of-the-art DES, as well as on their application to experimental optimization. Especially, it will discuss optimization efficiency, attainment of robust solutions, exploration of the actual search landscape, and the generalization into Pareto optimization of multiple objectives. Special emphasis will be put on a particular experimental platform employing DES at present times, namely Quantum Control experiments. The Quantum Control (QC) field aims at altering the course of quantum dynamics phenomena for specific target realizations, by means of closed-loop, adaptively learned laser pulses. The optimization task of QC experiments typically poses many algorithmic challenges, e.g., high-dimensionality, noise, constraints handling, and thus offers a rich domain for the development and application of specialized optimizers. Toward that end, the computational aspects of several real-world laboratory optimization case-studies will be presented. **This talk will be self-contained, and will target the general audience of CS, Engineering, and Applied Physics. It will not require any specialized background in Quantum Mechanics nor in Optimization.