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20192026
most citedOptimization meets Machine Learning: An Exact Algorithm for Semi-Supervised Support Vector Machines

6 citations · 18 across the 11 of their papers we have counts for

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12 papers · 1 filter

math.OC2026

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…

math.OC2025

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…

math.OC2025

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…

math.OC2025

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…

math.OC2024★ 2 cited

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…

math.OC2024

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…