We present an algorithm for triobjective nonlinear integer programs that combines the -constrained method with available oracles for biobjective integer programs. We prove that our method is able to detect the nondominated set within a finite number of iterations. Specific strategies to avoid the...
01a Articolo in rivista
-
-
In this paper, we propose a branch-and-bound algorithm for solving non convex quadratic programming problems with box constraints (BoxQP). Our approach combines existing tools, such as semidefinite programming (SDP) bounds strengthened through valid inequalities, with a new class of optimality-...
-
Support vector machines (SVMs) are well-studied supervised learning models for binary classification. Large amounts of samples can be cheaply and easily obtained in many applications. What is often a costly and error-prone process is to label these data points manually. Semi-supervised support...
-
Biclustering, also called co-clustering, block clustering, or two-way clustering, involves the simultaneous clustering of both the rows and the columns of a data matrix into distinct groups such that the rows and columns within a group display similar patterns. As a model problem for biclustering,...
-
Disorders of consciousness (DoC) are characterized by alteration in arousal and/or awareness commonly caused by severe brain injury. There exists a consensus on adopting advanced neuroimaging and electrophysiological procedures to improve diagnosis/prognosis of DoC patients. Currently, these...
-
In this paper we consider constrained optimization problems where both the objective and constraint functions are of the black-box type. Furthermore, we assume that the nonlinear inequality constraints are non-relaxable, i.e. their values and that of the objective function cannot be computed...
-
This paper is devoted to the analysis of worst case complexity bounds for linesearch-type derivative-free algorithms for the minimization of general non-convex smooth functions. We consider a derivative-free algorithm based on a linesearch extrapolation technique. First we prove that it enjoys the...
-
In this work we study high-probability bounds for stochastic subgradient methods under heavy tailed noise in Hilbert spaces. In this setting the noise is only assumed to have finite variance as opposed to a sub-Gaussian distribution for which it is known that standard subgradient methods enjoy high...
-
In the context of finite sums minimization, variance reduction techniques are widely used to improve the performance of state-of-the-art stochastic gradient methods. Their practical impact is clear, as well as their theoretical properties. Stochastic proximal point algorithms have been studied as...
-
Sequential Recommender Systems (SRSs) have emerged as a highly efficient approach to recommendation systems. By leveraging sequential data, SRSs can identify temporal patterns in user behaviour, significantly improving recommendation accuracy and relevance. Ensuring the reproducibility of these...