Search results for "Minification"

showing 10 items of 91 documents

Statistical criteria for early-stopping of support vector machines

2007

This paper proposes the use of statistical criteria for early-stopping support vector machines, both for regression and classification problems. The method basically stops the minimization of the primal functional when moments of the error signal (up to fourth order) become stationary, rather than according to a tolerance threshold of primal convergence itself. This simple strategy induces lower computational efforts and no significant differences are observed in terms of performance and sparsity.

Mathematical optimizationEarly stoppingStructured support vector machinebusiness.industryCognitive NeuroscienceMachine learningcomputer.software_genreRegressionProbability vectorComputer Science ApplicationsSupport vector machineRelevance vector machineArtificial IntelligenceConvergence (routing)MinificationArtificial intelligencebusinesscomputerMathematicsNeurocomputing
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A simulation/optimization model for selecting infrastructure alternatives in complex water resource systems

2010

The paper introduces a simulation/optimization procedure for the assessment and the selection of infrastructure alternatives in a complex water resources system, i.e. in a multisource (reservoirs) multipurpose bulk water supply scheme. An infrastucture alternative is here a vector X of n decision variables describing the candidate expansions/new plants/water transfers etc. Each parameter may take on a discrete number of values, with its own investment cost attached. The procedure uses genetic algorithms for the search of the optimal vector X through operators mimicking the mechanisms of natural selection. For each X, the value of the objective function (O.F.) is assessed via a simulation mo…

Mathematical optimizationEngineeringConservation of Natural ResourcesEnvironmental EngineeringUrban PopulationWater supplyInfrastructure optimizationWaste Disposal Fluidsimulation optimization water resource systemsResource AllocationWater PurificationResource (project management)Water SupplyHumansComputer SimulationTherapeutic IrrigationWater Science and TechnologyCost–benefit analysisbusiness.industrySimulation modelingEnvironmental resource managementModels TheoreticalInvestment (macroeconomics)DroughtsWater resourcesItalyMinificationbusinessAlgorithms
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Voltage Regulation and Power Losses Minimization in Automated Distribution Networks by an Evolutionary Multiobjective Approach

2004

In this paper, the problem of voltage regulation and power losses minimization for automated distribution systems is dealt with. The classical formulation of the problem of optimal control of shunt capacitor banks and Under Load Tap Changers located at HV/MV substations has been coupled with the optimal control of tie-switches and capacitor banks on the feeders of a large radially operated meshed distribution system with the aim of attaining minimum power losses and the flattening of the voltage profile. The considered formulation requires the optimization of two different objectives; therefore the use of adequate multiobjective heuristic optimization methods is needed. The heuristic strate…

Mathematical optimizationEngineeringbusiness.industryFuzzy setEnergy Engineering and Power TechnologyOptimal controlEvolutionary computationFlatteninglaw.inventionSettore ING-IND/33 - Sistemi Elettrici Per L'EnergiaCapacitorOptimal control optimization methods power distribution voltage control.Control theorylawMinificationVoltage regulationElectrical and Electronic EngineeringbusinessVoltage
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Optimal Set Points Regulation of Distributed Generation Units in Micro-grids under Islanded Operation

2010

The present work studies the problem of optimizing the power production levels of dispersed generation units in islanded microgrids. The problem is intrinsically multi-objective with non linear objectives and constraints, thus the solution approach is based on evolutionary optimization and uses the Non dominated Sorting Genetic Algorithm II. The objectives are calculated based on the solution of the load flow problem. The latter problem is more complicated when in the considered system a physical node with a sufficiently large production capability is not available, because all the generation node of the systems have similar and limited generation capability. In this paper, the issue has be…

Mathematical optimizationEngineeringbusiness.industryNode (networking)String (computer science)SortingMulti-objective optimizationSlack busSettore ING-IND/33 - Sistemi Elettrici Per L'EnergiaDistributed generationGenetic algorithmMinificationbusinessOptimal dispatch microgrids multi-objective optimization slack bus
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An Aggressive Search Procedure for the Bipartite Drawing Problem

1996

Graphs are used to represent reality in several areas of knowledge. This has generated considerable interest in graph drawing algorithms. Arc crossing minimization is a fundamental aesthetic criterion to obtain a readable map of a graph. The problem of minimizing the number of arc crossings in a bipartite graph (BDP) is NP-complete. In this paper we present an aggressive search scheme for the BDP based on the Intensification, Diversification and Strategic Oscillation elements of Tabu Search. Several algorithms can be obtained with this scheme by implementing different evaluators in the move definitions. In this paper we propose two variants. Computational results are reported on a set of 30…

Mathematical optimizationGraph drawingBipartite graphSearch procedureForce-directed graph drawingMinificationHeuristicsGraphTabu searchMathematics
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A Local Selection Algorithm for Switching Function Minimization

1984

The minimization algorithms which do not require any preliminary generation of all the prime implicants (PI's) of a function are the most efficient. In this work a new algorithm is described which follows such an approach. It is based on a local selection of PI's carried out by examining a set of vertices whose number is never greater than the number of PI's of a minimum cost cover. This algorithm takes advantage of a technique which uses numerical equivalents of the function vertices as pointers. For this reason it is well suited for implementation by computer. To illustrate the features of this algorithm a few examples are reported.

Mathematical optimizationImplicantProbability density functionFunction (mathematics)Theoretical Computer ScienceSet (abstract data type)Computational Theory and MathematicsCover (topology)Hardware and ArchitectureIndependent setAlgorithm designMinificationAlgorithmSoftwareMathematicsIEEE Transactions on Computers
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Greedy randomized adaptive search procedure with exterior path relinking for differential dispersion minimization

2015

We propose several new hybrid heuristics for the differential dispersion problem, the best of which consists of a GRASP with sampled greedy construction with variable neighborhood search for local improvement. The heuristic maintains an elite set of high-quality solutions throughout the search. After a fixed number of GRASP iterations, exterior path relinking is applied between all pairs of elite set solutions and the best solution found is returned. Exterior path relinking, or path separation, a variant of the more common interior path relinking, is first applied in this paper. In interior path relinking, paths in the neighborhood solution space connecting good solutions are explored betwe…

Mathematical optimizationInformation Systems and ManagementHeuristic (computer science)GRASPComputer Science ApplicationsTheoretical Computer ScienceSet (abstract data type)Artificial IntelligenceControl and Systems EngineeringPath (graph theory)MinificationHeuristicsSoftwareVariable neighborhood searchGreedy randomized adaptive search procedureMathematicsInformation Sciences
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NAUTILUS method: An interactive technique in multiobjective optimization based on the nadir point

2010

Most interactive methods developed for solving multiobjective optimization problems sequentially generate Pareto optimal or nondominated vectors and the decision maker must always allow impairment in at least one objective function to get a new solution. The NAUTILUS method proposed is based on the assumptions that past experiences affect decision makers’ hopes and that people do not react symmetrically to gains and losses. Therefore, some decision makers may prefer to start from the worst possible objective values and to improve every objective step by step according to their preferences. In NAUTILUS, starting from the nadir point, a solution is obtained at each iteration which dominates t…

Mathematical optimizationInformation Systems and ManagementInteractive programmingGeneral Computer Sciencebiologymedia_common.quotation_subjectManagement Science and Operations Researchbiology.organism_classificationMulti-objective optimizationIndustrial and Manufacturing EngineeringSightNegotiationIterated functionModeling and SimulationMinificationNautilusOptimal decisionMathematicsmedia_commonEuropean Journal of Operational Research
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Due Dates and RCPSP

2006

Due dates are an essential feature of real projects, but little effort has been made in studying the RCPSP with due dates in the activities. This paper tries to bridge this gap by studying two problems: the TardinessRCPSP, in which the objective is total tardiness minimization and the DeadlineRCPSP, in which the due dates are strict (deadlines) and the objective is makespan minimization. The first problem is NP-hard and the second is much harder, since finding a feasible solution is already NP-hard. This paper has three objectives: Firstly to compare the performance on both problems of well-known RCPSP heuristics - priority rules, sampling procedures and metaheuristics - with new versions w…

Mathematical optimizationJob shop schedulingbusiness.industryComputer scienceTardinessProfitability indexMinificationProject managementbusinessHeuristicsMetaheuristicGenerator (mathematics)
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A note on the Bregmanized Total Variation and dual forms

2009

This paper considers two approaches to perform image restoration while preserving the contrast. The first one is the Total Variation-based Bregman iterations while the second consists in the minimization of an energy that involves robust edge preserving regularization. We show that these two approaches can be derived form a common framework. This allows us to deduce new properties and to extend and generalize these two previous approaches.

Mathematical optimizationNoise measurementIterative methodCommon frameworkMinificationTotal variation denoisingAlgorithmRegularization (mathematics)Image restorationMathematics2009 16th IEEE International Conference on Image Processing (ICIP)
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