Search results for: Giorgio Gnecco

Items from 1 to 20 out of 53 results

article

Matrix completion of world trade: An analysis of interpretability through Shapley values

Giorgio Gnecco, Federico Nutarelli, Massimo Riccaboni

The World Economy > 46 > 9 > 2707 - 2731

Economic complexity and machine learning have recently become popular approaches for analysing international trade. However, for effective use of machine learning in relation to economic complexity and policymaking, it is important to understand what are the key features for predictions. In this framework, this article addresses the issue of the interpretability of results obtained with a machine...

article

Braess' paradox: A cooperative game‐theoretic point of view

Mauro Passacantando, Giorgio Gnecco, Yuval Hadas, Marcello Sanguineti

Networks > 78 > 3 > 264 - 283

Braess' paradox is a classical result in the theory of congestion games. It motivates theoretically why adding a resource (e.g., an arc) to a network may sometimes worsen, rather than improve, the overall network performance. Differently from previous literature, which studies Braess' paradox in a non‐cooperative game‐theoretic setting, in this work, a framework is proposed to investigate its occurrence...

article

Parameter estimation in a 3‐parameter p‐star random graph model

Pietro Lenarda, Giorgio Gnecco, Massimo Riccaboni

Networks > 77 > 3 > 403 - 420

An important issue in social network analysis refers to the development of algorithms for estimating parameters of a social network model, using data available from the network itself. This entails solving an optimization problem. In the paper, we propose a new method for parameter estimation in a specific social network model, namely, the so‐called p‐star random graph model with three parameters...

article

Some properties of transportation network cooperative games

Giorgio Gnecco, Yuval Hadas, Marcello Sanguineti

Networks > 74 > 2 > 161 - 173

A basic question in network analysis concerns the quantification of the importance of each node in terms of network connectivity. To this end, a possible approach consists in using cooperative game theory tools to define a measure of node centrality. In this paper, given a transportation network, a cooperative game model with transferable utility (TU game) is considered. The nodes of the network represent...

article

Complex frequency band structure of periodic thermo-diffusive materials by Floquet–Bloch theory

Andrea Bacigalupo, Maria Laura De Bellis, Giorgio Gnecco

Acta Mechanica > 2019 > 230 > 9 > 3339-3363

This work deals with the micromechanical study of periodic thermo-diffusive elastic multi-layered materials, which are of interest for the fabrication of solid oxide fuel cells. The focus is on the dynamic regime that is investigating the dispersive wave propagation within the periodic material. In this framework, a generalization of the Floquet–Bloch theory is adopted, able to determine the complex...

article

Neural approximations in discounted infinite-horizon stochastic optimal control problems

Giorgio Gnecco, Marcello Sanguineti

Engineering Applications of Artificial Intelligence > 2018 > 74 > C > 294-302

Neural approximations of the optimal stationary closed-loop control strategies for discounted infinite-horizon stochastic optimal control problems are investigated. It is shown that for a family of such problems, the minimal number of network parameters needed to achieve a desired accuracy of the approximate solution does not grow exponentially with the number of state variables. In such a way, neural-network...

article

An approach to transportation network analysis via transferable utility games

Yuval Hadas, Giorgio Gnecco, Marcello Sanguineti

Transportation Research Part B: Methodological > 2017 > 105 > C > 120-143

Network connectivity is an important aspect of any transportation network, as the role of the network is to provide a society with the ability to easily travel from point to point using various modes. A basic question in network analysis concerns how “important” each node is. An important node might, for example, greatly contribute to short connections between many pairs of nodes, handle a large amount...

article

A green policy to schedule tasks in a distributed cloud

Stefano Sebastio, Giorgio Gnecco

Optimization Letters > 2018 > 12 > 7 > 1535-1551

In the last years, demand and availability of computational capabilities experienced radical changes. Desktops and laptops increased their processing resources, exceeding users’ demand for large part of the day. On the other hand, computational methods are more and more frequently adopted by scientific communities, which often experience difficulties in obtaining access to the required resources....

article

Commitment-Based Equilibrium Environmental Strategies Under Time-Dependent Absorption Efficiency

Fouad El Ouardighi, Konstantin Kogan, Giorgio Gnecco, Marcello Sanguineti

Group Decision and Negotiation > 2018 > 27 > 2 > 235-249

This paper investigates how current and future generations are affected by commitment-based Nash equilibrium environmental strategies when the environmental absorption efficiency is susceptible to switch from a pollution sink to a source. We formulate a two-player differential game model of transboundary pollution that includes the environmental absorption efficiency as a state variable that can be...

article

Optimal distributed task scheduling in volunteer clouds

Stefano Sebastio, Giorgio Gnecco, Alberto Bemporad

Computers & Operations Research > 2017 > 81 > C > 231-246

The ever increasing request of computational resources has shifted the computing paradigm towards solutions where less computation is performed locally. The most widely adopted approach nowadays is represented by cloud computing. With the cloud, users can transparently access to virtually infinite resources with the same aptitude of using any other utility. Next to the cloud, the volunteer computing...

article

Optimal design of low-frequency band gaps in anti-tetrachiral lattice meta-materials

Andrea Bacigalupo, Giorgio Gnecco, Marco Lepidi, Luigi Gambarotta

Composites Part B: Engineering > 2017 > 115 > C > 341-359

The elastic wave propagation is investigated in a beam lattice material characterized by a square periodic cell with anti-tetrachiral microstructure. With reference to the Floquet-Bloch spectrum, focus is made on the band structure enrichments and modifications which can be achieved by equipping the cellular microstructure with tunable local resonators. By virtue of its composite mechanical nature,...

article

Symmetry and antisymmetry properties of optimal solutions to regression problems

Giorgio Gnecco

Optimization Letters > 2017 > 11 > 7 > 1427-1442

Besides requiring a good fit of the learned model to the empirical data, machine learning problems usually require such a model to satisfy additional constraints. Their satisfaction can be either imposed a-priori, or checked a-posteriori, once the optimal solution to the learning problem has been determined. In this framework, it is proved in the paper that the optimal solutions to several batch and...

article

A hierarchical consensus method for the approximation of the consensus state, based on clustering and spectral graph theory

Rita Morisi, Giorgio Gnecco, Alberto Bemporad

Engineering Applications of Artificial Intelligence > 2016 > 56 > C > 157-174

A hierarchical method for the approximate computation of the consensus state of a network of agents is investigated. The method is motivated theoretically by spectral graph theory arguments. In a first phase, the graph is divided into a number of subgraphs with good spectral properties, i.e., a fast convergence toward the local consensus state of each subgraph. To find the subgraphs, suitable clustering...

chapter

Regularization and Suboptimal Solutions in Learning from Data

Giorgio Gnecco, Marcello Sanguineti

Studies in Computational Intelligence > Innovations in Neural Information Paradigms and Applications > 113-154

Supervised learning from data is investigated from an optimization viewpoint. Ill-posedness issues of the learning problem are discussed and its Tikhonov, Ivanov, Phillips, and Miller regularizations are analyzed. Theoretical features of the optimization problems associated with these regularization techniques and their use in learning tasks are considered. Weight-decay learning is investigated, too...

chapter

Bounds for Approximate Solutions of Fredholm Integral Equations Using Kernel Networks

Giorgio Gnecco, Věra Kůrková, Marcello Sanguineti

Lecture Notes in Computer Science > Artificial Neural Networks and Machine Learning – ICANN 2011 > 126-133

Approximation of solutions of integral equations by networks with kernel units is investigated theoretically. There are derived upper bounds on speed of decrease of errors in approximation of solutions of Fredholm integral equations by kernel networks with increasing numbers of units. The estimates are obtained for Gaussian and degenerate kernels.

chapter

Smooth Optimal Decision Strategies for Static Team Optimization Problems and Their Approximations

Giorgio Gnecco, Marcello Sanguineti

Lecture Notes in Computer Science > SOFSEM 2010: Theory and Practice of Computer Science > Regular Papers > 440-451

Sufficient conditions for the existence and uniqueness of smooth optimal decision strategies for static team optimization problems with statistical information structure are derived. Approximation methods and algorithms to derive suboptimal solutions based on the obtained results are investigated. The application to network team optimization problems is discussed.

chapter

Some Comparisons of Model Complexity in Linear and Neural-Network Approximation

Giorgio Gnecco, Věra Kůrková, Marcello Sanguineti

Lecture Notes in Computer Science > Artificial Neural Networks – ICANN 2010 > Computational Intelligence > 358-367

Capabilities of linear and neural-network models are compared from the point of view of requirements on the growth of model complexity with an increasing accuracy of approximation. Upper bounds on worst-case errors in approximation by neural networks are compared with lower bounds on these errors in linear approximation. The bounds are formulated in terms of singular numbers of certain operators induced...

article

Supervised and semi-supervised classifiers for the detection of flood-prone areas

Giorgio Gnecco, Rita Morisi, Giorgio Roth, Marcello Sanguineti, more

Soft Computing > 2017 > 21 > 13 > 3673-3685

Supervised and semi-supervised machine-learning techniques are applied and compared for the recognition of the flood hazard. The learning goal consists in distinguishing between flood-exposed and marginal-risk areas. Kernel-based binary classifiers using six quantitative morphological features, derived from data stored in digital elevation models, are trained to model the relationship between morphology...

article

On the Curse of Dimensionality in the Ritz Method

Giorgio Gnecco

Journal of Optimization Theory and Applications > 2016 > 168 > 2 > 488-509

It is shown that the classical Ritz method of the calculus of variations suffers from the “curse of dimensionality,” i.e., an exponential growth, as a function of the number of variables, of the dimension a linear subspace needs in order to achieve a desired relative improvement in the accuracy of approximation of the optimal solution value. The proof is constructive and is obtained by exhibiting...

article

A SOM-based Chan–Vese model for unsupervised image segmentation

Mohammed M. Abdelsamea, Giorgio Gnecco, Mohamed Medhat Gaber

Soft Computing > 2017 > 21 > 8 > 2047-2067

Active Contour Models (ACMs) constitute an efficient energy-based image segmentation framework. They usually deal with the segmentation problem as an optimization problem, formulated in terms of a suitable functional, constructed in such a way that its minimum is achieved in correspondence with a contour that is a close approximation of the actual object boundary. However, for existing ACMs, handling...

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INFONA - science communication portal

Search results for: Giorgio Gnecco

Matrix completion of world trade: An analysis of interpretability through Shapley values

Braess' paradox: A cooperative game‐theoretic point of view

Parameter estimation in a 3‐parameter p‐star random graph model

Some properties of transportation network cooperative games

Complex frequency band structure of periodic thermo-diffusive materials by Floquet–Bloch theory

Neural approximations in discounted infinite-horizon stochastic optimal control problems

An approach to transportation network analysis via transferable utility games

A green policy to schedule tasks in a distributed cloud

Commitment-Based Equilibrium Environmental Strategies Under Time-Dependent Absorption Efficiency

Optimal distributed task scheduling in volunteer clouds

Optimal design of low-frequency band gaps in anti-tetrachiral lattice meta-materials

Symmetry and antisymmetry properties of optimal solutions to regression problems

A hierarchical consensus method for the approximation of the consensus state, based on clustering and spectral graph theory

Regularization and Suboptimal Solutions in Learning from Data

Bounds for Approximate Solutions of Fredholm Integral Equations Using Kernel Networks

Smooth Optimal Decision Strategies for Static Team Optimization Problems and Their Approximations

Some Comparisons of Model Complexity in Linear and Neural-Network Approximation

Supervised and semi-supervised classifiers for the detection of flood-prone areas

On the Curse of Dimensionality in the Ritz Method

A SOM-based Chan–Vese model for unsupervised image segmentation

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