SOURCE-LINKED INTELLIGENCE

Challenges in Competitive Online Optimisation

CORDIS · observation · Publication date unknown

ever, several recent discoveries of new algorithmic design and analysis techniques have opened up novel avenues for overcoming previous obstacles. Alongside these technical advancements, the rise of machine learning is now significantly enriching our toolset for dealing with uncertainty. This has motivated the recent emergence of the field of learning-augmented algorithms. Here, an algorithm's input is augmented with predictions, aiming for near-optimal performance if predictions are reasonably good, while still retaining classical worst-case guarantees even for highly erroneous predictions. Inspired by these recent developments, this project aims to substantially elevate our understanding of decision-making under uncertainty. The main objectives are (1) to explore new directions around the concept of work functions, (2) to elevate the mirror descent technique into a generic tool for online algorithm design, (3) to develop universal techniques for designing learning-augmented algorithms, and (4) to expand the scope of learning-augmented algorithms to new domains. The project addre

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recordType

award

status

SIGNED

region

EU

value

1499828

unit

EUR