1ST MINOA PHD SCHOOL "MIXEDINTEGER NONLINEAR OPTIMIZATION MEETS DATA SCIENCE"The school will cover the main topics interlacing Optimization and Data Science and will be held by eminent international researchers:
Deep learning for AI (Yoshua Bengio, University of Montreal, Canada)
Unsupervised Learning and Clustering (Ravi Kumar, Google inc., USA)
Learning for Optimization (Andrea Lodi, Politechnique de Montreal, Canada)
Support Vector Machines (Laura Palagi, Università "La Sapienza" di Roma, Italia).
The school will also include two industrial demonstrations given by two companies participating to the MINOA project: ORTEC (Joaquim Gromicho, University of Amsterdam) and by IBM France (Pierre Bonami).
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A TRAFFIC CONTROL SYSTEM BASED ON LOGIC PROGRAMMINGA sophisticated control system for urban traffic control has been designed, implemented and tested. The system modifies in real time the phase length of the traffic controller in order to optimize the throughput at each intersection. The kernel of the system is a logic programming engine that interacts with the traffic controller and with sensors that measure the traffic conditions in real time.
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AUSSOIS COMBINATORIAL OPTIMIZATION WORKSHOPStarting 1999, every second year we coorganize the Aussois Combinatorial Optimization Workshop with the University of Cologne and the University of Heidelberg. The Workshop aims at putting together senior and your specialists in the field of Discrete Mathematical Optimization.
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CARGESE WORKSHOP ON COMBINATORIAL OPTIMIZATION
The yearly Cargese workshop aims to bring together researchers in combinatorial optimization around a chosen topic of current interest. It is intended to be a forum for the exchange of recent developments and powerful tools, with an emphasis on theory. Other goals include: the promotion of interactions between participants and discussion of open problems.
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COST/MINO PHD SCHOOL ON ADVANCED OPTIMIZATION METHODS 2016
The school will cover the main advances in four topics in Optimization and
will be held by four eminent international researchers:
Polyhedral Combinatorics (Santanu S. Dey  Georgia Tech, USA), Interior Point Methods (Jordi Castro  Università Politecnica della Catalogna, Barcellona, Spagna), Structured DantzigWolfe Decomposition (Antonio Frangioni, Università di Pisa), Semidefinite Programming (Veronica Piccialli, Università "Tor Vergata" di Roma).
The school will take place in Rome, Via dei Taurini 19, Aula Piano Terra
June 610, 2016.
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DERIVATIVEFREE OPTIMIZATION

This focus is devoted to numerical methods for the solution of optimization problems where first and higher order derivatives of the functions defining the problems are not available (or not trustworthy). All of the presented methods are available for download under the GNU GPL license through the DerivativeFree Library (DFL). The library is constantly kept updated and new software packages are added to the library as soon as they are developped by the DFL team of developpers and contributors. DFL is administered by Giampaolo Liuzzi
At the moment, the packages that can be downloaded (for free) are the following:

 Local Optimization
 SDBOX and DFBOX_IMPR, for bound constrained problems
 SDPEN, for general (inequality/equality) constrained problems
 Global Optimization
 ACRS, for bound constrained problems
 DDFSA, for bound constrained problems
 DFSA, for general (inequality/equality) constrained problems
 DIRMIN, for bound constrained problems
 DIRDFN, for general constrained problems
 DIRECT, for bound constrained problems
 Test Problems for Global Optimization
 TESTGO, a collection of problems for global optimization
 Nonsmooth Optimization and Minimax Problems
 DFN, for nonsmooth constrained problems
 SDMINMAX, for finite minimax problems
 Mixed Integer Nonlinear Programming
 DFLBOX, for bound constrained problems
 DFLGEN, for general (inequality/equality) constrained problems
 DFLINT, for general constrained integer problems
 Multiobjective optimization
 DFMO, for general (inequality/equality) constrained nonsmooth and multiobjective problems
 MOIF, for box constrained multiobjective problems
 MODIR, for constrained multiobjective global optimization problems
 TESTMO, A collection of bound constrained problems for multiobjective optimization (original AMPL versions of the problems are also available and provided by Prof. L.N. Vicente and coauthors and can be found at this URL)
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EFFICIENZA ENERGETICA: PRODUZIONE, DISTRIBUZIONE E CONSUMO DI ENERGIA ELETTRICA.Lo IASI si occupa di attività di modellazione dei processi industriali energetici, di progettazione e sviluppo di algoritmi per risolvere problemi di ottimizzazione della gestione operativa di centrali elettriche nel breve periodo (Unit Commitment), di progettazione di reti di distribuzione con attenzione alla sicurezza del sistema, di gestione intelligente della rete di distribuzione (Optimal Transmission Switching), di modelli per la gestione dei mercati elettrici, di modelli e algoritmi per la determinazione di strategie per la determinazione delle offerte delle compagnie produttrici nelle aste dei mercati elettrici.
NETWORK OPTIMIZATION

Network optimization studies network related problems which include connecting sites, edge dimensioning (capacity installation) and traffic engineering (routing), possibly in presence of demand uncertainty (robustness), component failures (survivability) and energyrelated issues. It models problems arising in several domains: ICT, energy management, transportation, logistics, project management and other areas. Different versions of the problem are studied. More information at the Network Optimization at IASI webpage 
NETWORK OPTIMIZATION IN A SINGLECELL ORGANISM (2012)
Experimental studies have shown that the slime mold Physarum polycephalum, a singlecell slime mold that feeds on bacteria and spores, is able to perform tasks that are surprisingly complex for such a simple organism: for example, finding the shortest path through a maze. From these experiments and from the mathematical model proposed by biologists, IASI researchers have developed a rigorous mathematical analysis that confirms how the process followed by Physarum is a perfect “natural algorithm” for network optimization, developed by evolution over millions of years.
In the experiments initially performed at the Hokkaido university in Japan, the slime mold was uniformly distributed over a maze where two oat flakes have been positioned; oat is the slime mold's favorite food. With the passage of time, the slime mold retracted from the less efficient paths, and concentrated its mass on the shortest route joining the two oat flakes. The study analyzed the biological mechanism that reconfigures the slime mold into the shortest path: each “vein” of the Physarum expands or contracts depending on higher or lower availability of nutrients, following precise equations identified by the biologists. In turn, the larger or smaller dilation of the veins implies a variation of the flux passing through them, thus creating a dynamical process.
The study carried out by IASI researchers in collaboration with the Max Planck Institute for Informatics in Saarbruecken (Germany) and presented at the Symposium on Discrete Algorithms in Kyoto, has clarified how network optimization is a mathematical consequence of the vein dynamics, independent of the complexity of the underlying network. One could say that evolution, during millions of years, designed Physarum's vein regulation mechanism to obtain the right algorithm for finding the shortest path in a network.
This type of research has two goals. One is to understand the mechanisms underlying “intelligent” behavior in the simplest of organisms; without this first step, one cannot hope to understand the same mechanisms in more evolved organisms, such as animals or men. The second is to explore alternative, potentially fruitful approaches for challenging network optimization problems, such as the design of connectivity networks of low total cost.
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OPTIMIZATION OF CONTAINER LOGISTICS
In container logistic, the Block Relocation Problem (BRP) consists in deciding how to empty a container yard in order to minimize the total number of reshuffle operations. We are interested in developing new heuristic and exact methods for theBRP. We also study the computational complexity of some related problems.
OPTIMIZED LOGIC MINING FOR LARGE SCALE LEARNING FROM DATADMB is an integrated data mining tool designed with the purpose of extracting knowledge in logic form from large amount of data. The method is based on the formulation of two hard integer programming problems (a set coveringlike method that models the feature selection problem and a minimum cost satisfiability problem that models the classification problem). DMB can be accessed by a friendly web interface and is particularly suited for data mining problems where the number of features is very large, as in the case of genetic data.