104 citations · 299 across the 16 of their papers we have counts for
17 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…
Exact and Heuristic Algorithms for Constrained Biclustering
Antonio M. Sudoso
Biclustering, also known as co-clustering or two-way clustering, simultaneously partitions the rows and columns of a data matrix to reveal submatrices with coherent patterns. Incor…
Optimal Placement of Nature-Based Solutions for Urban Challenges
Diego Maria Pinto, Davide Donato Russo, Antonio M. Sudoso
Increased urbanization and climate change intensify urban heat islands and degrade air quality, making current mitigation strategies insufficient. Nature-based solutions (NBSs), su…
Strong bounds for large-scale Minimum Sum-of-Squares Clustering
Anna Livia Croella, Veronica Piccialli, Antonio M. Sudoso
Clustering is a fundamental technique in data analysis and machine learning, used to group similar data points together. Among various clustering methods, the Minimum Sum-of-Square…
A column generation algorithm with dynamic constraint aggregation for minimum sum-of-squares clustering
Antonio M. Sudoso, Daniel Aloise
The minimum sum-of-squares clustering problem (MSSC), also known as -means clustering, refers to the problem of partitioning data points into clusters, with the objectiv…
A Semidefinite Programming-Based Branch-and-Cut Algorithm for Biclustering
Antonio M. Sudoso
Biclustering, also called co-clustering, block clustering, or two-way clustering, involves the simultaneous clustering of both the rows and columns of a data matrix into distinct g…
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…
Optimizing accuracy and diversity: a multi-task approach to forecast combinations
Giovanni Felici, Antonio M. Sudoso
We present a multi-task optimization approach based on a deep learning architecture for time series forecasting. We leverage large collections of time series to identify the weight…
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…
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…
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…
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…