1,721,200 research outputs found
Manuale pratico di oncologia. Fondamenti per il medico non oncologo- Costanzo Francesco; Cognetti Francesco; Benedetti Panici Pierluigi
No full textBenedetti Panici P, Di Donato V., Fischetti M, Lo Prete E, Musella A, Plotti F, Casorelli A, Bellati F (2011).
Capitoli: Carcinoma Dell' Endometrio.. arcinoma Epiteliale Dell’ovaio In: -. Manuale Pratico Di Oncologia Fondamenti Per Il Medico Non Oncologo
From Mixed-Integer Linear to Mixed-Integer Bilevel Linear Programming
No full textBilevel Optimization is a very challenging framework where two players (with different objectives) compete for the definition of the final solution. In this paper we address a generic mixed-integer bilevel linear program, i.e., a bilevel optimization problem where the objective functions and constraints are all linear, and some variables are required to take integer values. We briefly describe some main ingredients of a branch-and-cut general-purpose framework for the exact solution (under appropriate assumptions) of mixed-integer bilevel linear programs
The New Generation of OR Enthusiasts: the AIROYoung Experiment
Full text linkThis special issue is dedicated to young researchers in operations research (OR) and to the newest topics of research in this field. To better understand the new generations of OR enthusiasts, what they are working on, and how they are going to feel part of this research community, the issue is dedicated to the 4th AIROYoung Workshop (Bolzano, 5–7 February 2020) and to the Italian AIROYoung chapter experience. AIROYoung is an example of a successful initiative that was born “from young researchers for young researchers”: volunteering PhDs and young OR specialists put their time and energies to create a young community, offer resources, and foster the collaboration between universities and industries. The issue will contain both scientific papers on the newest research topics, and extra contents and interviews to picture the community behind AIROYoung, and why it is important for young OR researchers around the world to feel part of a community
Local branching
No full textThe availability of effective exact or heuristic solution methods for general Mixed-Integer Programs (MIPs) is of paramount importance for practical applications. In the present paper we investigate the use of a generic MIP solver as a black-box "tactical" tool to explore effectively suitable solution subspaces defined and controlled at a "strategic" level by a simple external branching framework. The procedure is in the spirit of well-known local search metaheuristics, but the neighborhoods are obtained through the introduction in the MIP model of completely general linear inequalities called local branching cuts. The new solution strategy is exact in nature, though it is designed to improve the heuristic behavior of the MIP solver at hand. It alternates high-level strategic branchings to define the solution neighborhoods, and low-level tactical branchings to explore them. The result is a completely general scheme aimed at favoring early updatings of the incumbent solution, hence producing high-quality solutions at early stages of the computation. The method is analyzed computationally on a large class of very difficult MIP problems by using the state-of-the-art commercial software ILOG-Cplex 7.0 as the black-box tactical MIP solver. For these instances, most of which cannot be solved to proven optimality in a reasonable time, the new method exhibits consistently an improved heuristic performance: in 23 out of 29 cases, the MIP solver produced significantly better incumbent solutions when driven by the local branching paradigm. © 2003 Springer-Verlag
A Branch-and-Cut Algorithm for the Resource-Constrained Minimum-Weight Arborescence Problem
No full textIn this paper, we present a branch-and-cut algorithm for the exact solution of an NP-hard extension of the well-known Minimum-Weight Arborescence (MWA) problem, in which resource constraints for each node are considered. This Resource-Constrained Minimum-Weight Arborescence (RMWA) problem arises, e.g., in the design of distribution networks in which finite resources are available at each node. Some main classes of cuts are described, and the corresponding separation algorithms are presented. Also, we outline a procedure to extend to RMWA some known classes of valid inequalities for the asymmetric traveling salesman problem. New heuristic procedures to compute near-optimal feasible solutions are proposed, which proved to be very effective to reduce the overall computing time spent by the branch-and-cut algorithm. Computational experience on three classes of test problems involving up to 500 vertices is reported, showing that the proposed approach outperforms other published methods. © 1997 John Wiley & Sons, Inc
On the knapsack closure of 0-1 integer linear programs
Full text linkMany inequalities for Mixed-Integer Linear Programs (MILPs) or pure Integer Linear Programs (ILPs) are derived from the Gomory corner relaxation, where all the nonbinding constraints at an optimal LP vertex are relaxed. Computational results show that the corner relaxation gives a good approximation of the integer hull for problems with general-integer variables, but the approximation is less satisfactory for problems with 0-1 variables only. A possible explanation is that, for 0-1 ILPs, even the non-binding variable bound constraints xj≥0 or xj≤1 play an important role, hence their relaxation produces weaker bounds.In this note we address a relaxation for 0-1 ILPs that explicitly takes all variable bound constraints into account. More specifically, we introduce the concept of knapsack closure as a tightening of the classical Chvátal-Gomory (CG) closure. The knapsack closure is obtained as follows: for all inequalities wTx≥w0 valid for the LP relaxation, add to the original system all the valid inequalities for the knapsack polytope conv{xε{0,1}n:wTx≥w0}. A MILP model for the corresponding separation problem is also introduced. © 2010 Elsevier B.V
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface
No full textExact algorithms for minimum routing cost trees
No full textGiven a set of points and distances between them, a basic problem in network
design calls for selecting a graph connecting them at minimum total
{\em routing cost}, i.e., the sum over all pairs of points of
the length of their shortest path in the graph.
In this paper we describe some branch and bound algorithms for the exact solution
of a relevant special case arising when the graph has to be a tree.
One of the enhancements to our algorithms is the use of ``LP shortcutting'',
which we introduce as a general purpose technique for speeding up the search.
Besides network design, we show how trees of
small routing cost find useful application
in computational
biology, where they can be used to determine good alignments
of genomic sequences. This leads to a novel alignment heuristic
that we analyze in our computational section
Learning MILP Resolution Outcomes Before Reaching Time-Limit
No full textThe resolution of some Mixed-Integer Linear Programming (MILP) problems still presents challenges for state-of-the-art optimization solvers and may require hours of computations, so that a time-limit to the resolution process is typically provided by a user. Nevertheless, it could be useful to get a sense of the optimization trends after only a fraction of the specified total time has passed, and ideally be able to tailor the use of the remaining resolution time accordingly, in a more strategic and flexible way. Looking at the evolution of a partial branch-and-bound tree for a MILP instance, developed up to a certain fraction of the time-limit, we aim to predict whether the problem will be solved to proven optimality before timing out. We exploit machine learning tools, and summarize the development and progress of a MILP resolution process to cast a prediction within a classification framework. Experiments on benchmark instances show that a valuable statistical pattern can indeed be learned during MILP resolution, with key predictive features reflecting the know-how and experience of field’s practitioners
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