Search results for "Mathematical optimization"

showing 10 items of 1300 documents

Solution to nonlinear MHDS arising from optimal growth problems

2011

Abstract In this paper we propose a method for solving in closed form a general class of nonlinear modified Hamiltonian dynamic systems (MHDS). This method is used to analyze the intertemporal optimization problem from endogenous growth theory, especially the cases with two controls and one state variable. We use the exact solutions to study both uniqueness and indeterminacy of the optimal path when the dynamic system has not a well-defined isolated steady state. With this approach we avoid the linearization process, as well as the reduction of dimension technique usually applied when the dynamic system offers a continuum of steady states or no steady state at all.

Mathematical optimizationState variableSteady state (electronics)Sociology and Political ScienceGeneral Social SciencesReduction (complexity)Nonlinear systemLinearizationPath (graph theory)UniquenessStatistics Probability and UncertaintyGeneral PsychologyHamiltonian (control theory)MathematicsMathematical Social Sciences
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On the evaluation of the global heat transfer coefficient in cutting

2007

The use of numerical simulations for investigating machining processes is remarkably increasing because of the simulation cost is lower than the experiments and the possibility to analyze local variables such as pressures, strains, and temperatures is allowable. Process simulation is very hard from a computational point of view, since it frequently requires remeshing phases and very small time steps. As a consequence, the simulated cutting time is usually of the order of few milliseconds and no steady cutting conditions are generally achieved, at least as far as thermal conditions are concerned. Therefore, nowadays numerical prediction of cutting temperatures cannot be considered fully reli…

Mathematical optimizationSteady stateMechanical EngineeringRakeMODELSMechanicsHeat transfer coefficientPressure coefficientIndustrial and Manufacturing EngineeringFinite element methodTOOL WEARMachiningTEMPERATURE DISTRIBUTIONHeat transferSIMULATIONProcess simulationFINITE-ELEMENT-ANALYSISSettore ING-IND/16 - Tecnologie E Sistemi Di LavorazioneMathematics
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Optimal Impulse Control When Control Actions Have Random Consequences

1997

We consider a generalised impulse control model for controlling a process governed by a stochastic differential equation. The controller can only choose a parameter of the probability distribution of the consequence of his control action which is therefore random. We state optimality results relating the value function to quasi-variational inequalities and a formal optimal stopping problem. We also remark that the value function is a viscosity solution of the quasi-variational inequalities which could lead to developments and convergence proofs of numerical schemes. Further, we give some explicit examples and an application in financial mathematics, the optimal control of the exchange rate…

Mathematical optimizationStochastic differential equationControl theoryGeneral MathematicsBellman equationMathematical financeProbability distributionOptimal stoppingManagement Science and Operations ResearchViscosity solutionOptimal controlComputer Science ApplicationsMathematicsMathematics of Operations Research
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Hedging of Spatial Temperature Risk with Market-Traded Futures

2011

The main objective of this work is to construct optimal temperature futures from available market-traded contracts to hedge spatial risk. Temperature dynamics are modelled by a stochastic differential equation with spatial dependence. Optimal positions in market-traded futures minimizing the variance are calculated. Examples with numerical simulations based on a fast algorithm for the generation of random fields are presented.

Mathematical optimizationStochastic differential equationWork (thermodynamics)Random fieldApplied MathematicsStochastic simulationEconometricsVariance (accounting)Spatial dependenceHedge (finance)Futures contractFinanceMathematicsApplied Mathematical Finance
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A novel technique for stochastic root-finding: Enhancing the search with adaptive d-ary search

2017

The most fundamental problem encountered in the field of stochastic optimization, is the Stochastic Root Finding (SRF) problem where the task is to locate an unknown point x∗ for which g(x∗) = 0 for a given function g that can only be observed in the presence of noise [15]. The vast majority of the state-of-the-art solutions to the SRF problem involve the theory of stochastic approximation. The premise of the latter family of algorithms is to oper ate by means of so-called “small-step”processesthat explorethe search space in a conservative manner. Using this paradigm, the point investigated at any time instant is in the proximity of the point investigated at the previous time instant, render…

Mathematical optimizationStochastic point location problemsInformation Systems and ManagementLearning automataComputer scienceStochastic root finding problemsLearning Automata020206 networking & telecommunications02 engineering and technologyInterval (mathematics)Function (mathematics)Stochastic approximationComputer Science ApplicationsTheoretical Computer ScienceArtificial IntelligenceControl and Systems Engineering0202 electrical engineering electronic engineering information engineeringSearch problem020201 artificial intelligence & image processingStochastic optimizationAlgorithmRoot-finding algorithmSoftwareInformation Sciences
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Approximation-Based Adaptive Fuzzy Tracking Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Time-Delay Systems

2015

This paper focuses on the problem of approximation-based adaptive fuzzy tracking control for a class of stochastic nonlinear time-delay systems with a nonstrict-feedback structure. A variable separation approach is introduced to overcome the design difficulty from the nonstrict-feedback structure. Mamdani-type fuzzy logic systems are utilized to model the unknown nonlinear functions in the process of controller design, and an adaptive fuzzy tracking controller is systematically designed by using a backstepping technique. It is shown that the proposed controller guarantees that all signals in the closed-loop system are fourth-moment semiglobally uniformly ultimately bounded, and the tracking…

Mathematical optimizationStochastic processApplied MathematicsFuzzy logicTracking errorNonlinear systemComputational Theory and MathematicsArtificial IntelligenceControl and Systems EngineeringControl theoryBacksteppingAdaptive systemBounded functionMathematicsIEEE Transactions on Fuzzy Systems
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Average flow constraints and stabilizability in uncertain production-distribution systems

2009

We consider a multi-inventory system with controlled flows and uncertain demands (disturbances) bounded within assigned compact sets. The system is modelled as a first-order one integrating the discrepancy between controlled flows and demands at different sites/nodes. Thus, the buffer levels at the nodes represent the system state. Given a long-term average demand, we are interested in a control strategy that satisfies just one of two requirements: (i) meeting any possible demand at each time (worst case stability) or (ii) achieving a predefined flow in the average (average flow constraints). Necessary and sufficient conditions for the achievement of both goals have been proposed by the aut…

Mathematical optimizationStochastic stabilityControl and OptimizationComputer scienceSCHEDULING POLICIESUNKNOWN INPUTSInventory control; Robust controlRobust controlUncertain systemsUncertain demandsManagement Science and Operations ResearchControl strategies; Inventory systems; Uncertain demands; Worst caseStability (probability)Distribution systemMULTI-INVENTORY SYSTEMSControl theoryProduction (economics)Inventory control Robust control Stochastic stabilityAverage costInventory systemsMathematicsInventory controlStochastic processControl strategiesApplied MathematicsWorst caseNETWORKSControllabilityFlow (mathematics)Bounded functionProduction controlRobust controlSettore MAT/09 - Ricerca OperativaMANUFACTURING SYSTEMS
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Solving the Discrete Multiple Criteria Problem using Convex Cones

1984

An interactive method employing pairwise comparisons of attainable solutions is developed for solving the discrete, deterministic multiple criteria problem assuming a single decision maker who has an implicit quasi-concave increasing utility (or value) function. The method chooses an arbitrary set of positive multipliers to generate a proxy composite linear objective function which is then maximized over the set of solutions. The maximizing solution is compared with several solutions using pairwise judgments asked of the decision maker. Responses are used to eliminate alternatives using convex cones based on expressed preferences, and then a new set of weights is found that satisfies the i…

Mathematical optimizationStrategy and ManagementRegular polygonMultiple criteriaPairwise comparisonManagement Science and Operations ResearchDecision makerProxy (statistics)Mathematical proofMathematicsDecision analysismultiattribute programming: multiple criteria convex cones [decision analysis utility/preference]Management Science
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Detecting All Dependences in Systems of Geometric Constraints Using the Witness Method

2007

In geometric constraints solving, the detection of dependences and the decomposition of the system into smaller subsystems are two important steps that characterize any solving process, but nowadays solvers, which are graph-based in most of the cases, fail to detect dependences due to geometric theorems and to decompose such systems. In this paper, we discuss why detecting all dependences between constraints is a hard problem and propose to use the witness method published recently to detect both structural and non structural dependences.We study various examples of constraints systems and show the promising results of the witness method in subtle dependences detection and systems decomposi…

Mathematical optimizationStructural dependenceGraph (abstract data type)Geometric theoremAlgorithmWitnessMathematics
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A Compact Representation of Preferences in Multiple Criteria Optimization Problems

2019

A critical step in multiple criteria optimization is setting the preferences for all the criteria under consideration. Several methodologies have been proposed to compute the relative priority of criteria when preference relations can be expressed either by ordinal or by cardinal information. The analytic hierarchy process introduces relative priority levels and cardinal preferences. Lexicographical orders combine both ordinal and cardinal preferences and present the additional difficulty of establishing strict priority levels. To enhance the process of setting preferences, we propose a compact representation that subsumes the most common preference schemes in a single algebraic object. We …

Mathematical optimizationSubjective preferencesECONOMIA APLICADAOptimization problemComputer scienceProcess (engineering)020209 energyGeneral MathematicsAnalytic hierarchy processContext (language use)02 engineering and technologyLexicographic orders0202 electrical engineering electronic engineering information engineeringComputer Science (miscellaneous)powersetRepresentation (mathematics)Engineering (miscellaneous)Preference (economics)analytic hierarchy processPowersetAnalytic hierarchy processlcsh:Mathematicslcsh:QA1-939Lexicographical orderObject (computer science)subjective preferencessubjective preferences; analytic hierarchy process; lexicographic orders; powerset12.- Garantizar las pautas de consumo y de producción sostenibles16.- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible facilitar acceso a la justicia para todos y crear instituciones eficaces responsables e inclusivas a todos los niveleslexicographic orders020201 artificial intelligence & image processingECONOMIA FINANCIERA Y CONTABILIDAD
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