## Thesis etd-01202022-122248 |

Thesis type

Tesi di dottorato di ricerca

Author

IOMMAZZO, GABRIELE

URN

etd-01202022-122248

Thesis title

Algorithmic Configuration by Learning and Optimization

Academic discipline

INF/01

Course of study

INFORMATICA

Supervisors

**tutor**Prof. Frangioni, Antonio

Keywords

- machine learning
- operations research
- optimization solvers
- algorithm configuration
- parameter tuning
- distance geometry
- mathematical programming
- mathematical optimization

Graduation session start date

06/02/2022

Availability

Full

Summary

The research topics of this Ph.D. thesis lie at the intersection of Machine Learning (ML) and Mathematical Programming (MP). The main contributions concern the algorithm configuration problem and the distance geometry problem.

Given a configurable algorithm A and an input P for A, the algorithm configuration problem (ACP) addresses the issue of identifying the parameter configuration of A ensuring the best algorithmic performance in solving P. We propose two novel MP-driven methodologies, where: we first train an ML predictor to approximate the behaviour of A; then, we provide an explicit description of its mathematical properties and embed the resulting encoding into an MP formulation F; finally, when we need to use A on a new input, we optimize F to retrieve the parameter configuration of A yielding the best algorithmic performance for that input.

Furthermore, we consider a methodology for finding a realization of a graph in a Euclidean space of given dimension. This is known as the distance geometry problem, and we propose a new MP formulation for solving it. Our research is partly motivated by the fact that it can serve as a graph embedding methodology, in view of applying vector-based ML paradigms to graphs.

Given a configurable algorithm A and an input P for A, the algorithm configuration problem (ACP) addresses the issue of identifying the parameter configuration of A ensuring the best algorithmic performance in solving P. We propose two novel MP-driven methodologies, where: we first train an ML predictor to approximate the behaviour of A; then, we provide an explicit description of its mathematical properties and embed the resulting encoding into an MP formulation F; finally, when we need to use A on a new input, we optimize F to retrieve the parameter configuration of A yielding the best algorithmic performance for that input.

Furthermore, we consider a methodology for finding a realization of a graph in a Euclidean space of given dimension. This is known as the distance geometry problem, and we propose a new MP formulation for solving it. Our research is partly motivated by the fact that it can serve as a graph embedding methodology, in view of applying vector-based ML paradigms to graphs.

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