104 citations · 298 across the 13 of their papers we have counts for
14 papers
Global Optimization of Mixed-Integer Bound-Constrained Quadratic Fractional Problems
Marco Locatelli, Veronica Piccialli, Antonio M. Sudoso
We study mixed-integer quadratic fractional optimization problems with binary and continuous box-constrained variables. The objective is the ratio of two quadratic functions, a str…
Beyond binarity: Semidefinite programming for ternary quadratic problems
Frank de Meijer, Veronica Piccialli, Renata Sotirov +1
We study the ternary quadratic problem (TQP), a quadratic optimization problem with linear constraints where the variables take values in . While semidefinite program…
Optimization meets Machine Learning: An Exact Algorithm for Semi-Supervised Support Vector Machines
Veronica Piccialli, Jan Schwiddessen, Antonio M. Sudoso
Support vector machines (SVMs) are well-studied supervised learning models for binary classification. In many applications, large amounts of samples can be cheaply and easily obtai…
Predicting municipalities in financial distress: a machine learning approach enhanced by domain expertise
Dario Piermarini, Antonio M. Sudoso, Veronica Piccialli
Financial distress of municipalities, although comparable to bankruptcy of private companies, has a far more serious impact on the well-being of communities. For this reason, it is…
Supervised Feature Compression based on Counterfactual Analysis
Veronica Piccialli, Dolores Romero Morales, Cecilia Salvatore
Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning. For a given classifier and an instance classified in an undesired class, it…
Fix and Bound: An efficient approach for solving large-scale quadratic programming problems with box constraints
Marco Locatelli, Veronica Piccialli, Antonio M. Sudoso
In this paper, we propose a branch-and-bound algorithm for solving nonconvex quadratic programming problems with box constraints (BoxQP). Our approach combines existing tools, such…
Global Optimization for Cardinality-constrained Minimum Sum-of-Squares Clustering via Semidefinite Programming
Veronica Piccialli, Antonio M. Sudoso
The minimum sum-of-squares clustering (MSSC), or k-means type clustering, has been recently extended to exploit prior knowledge on the cardinality of each cluster. Such knowledge i…
An Exact Algorithm for Semi-supervised Minimum Sum-of-Squares Clustering
Veronica Piccialli, Anna Russo Russo, Antonio M. Sudoso
The minimum sum-of-squares clustering (MSSC), or k-means type clustering, is traditionally considered an unsupervised learning task. In recent years, the use of background knowledg…
Mixed-Integer Nonlinear Programming for State-based Non-Intrusive Load Monitoring
Marco Balletti, Veronica Piccialli, Antonio M. Sudoso
Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the energy consumption of each appliance given the aggregate signal…
SOS-SDP: an Exact Solver for Minimum Sum-of-Squares Clustering
Veronica Piccialli, Antonio M. Sudoso, Angelika Wiegele
The minimum sum-of-squares clustering problem (MSSC) consists of partitioning observations into clusters in order to minimize the sum of squared distances from the points t…
Improving P300 Speller performance by means of optimization and machine learning
Luigi Bianchi, Chiara Liti, Giampaolo Liuzzi +2
Brain-Computer Interfaces (BCIs) are systems allowing people to interact with the environment bypassing the natural neuromuscular and hormonal outputs of the peripheral nervous sys…
A machine learning approach for forecasting hierarchical time series
Paolo Mancuso, Veronica Piccialli, Antonio M. Sudoso
In this paper, we propose a machine learning approach for forecasting hierarchical time series. When dealing with hierarchical time series, apart from generating accurate forecasts…
Computing mixed strategies equilibria in presence of switching costs by the solution of nonconvex QP problems
Giampaolo Liuzzi, Marco Locatelli, Veronica Piccialli +1
In this paper we address game theory problems arising in the context of network security. In traditional game theory problems, given a defender and an attacker, one searches for mi…
Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network
Veronica Piccialli, Antonio M. Sudoso
Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate p…