Search results for "optimal"

showing 10 items of 706 documents

Gauss-Type Quadrature Formulae for Parabolic Splines with Equidistant Knots

2010

We construct Gauss, Lobatto, and Radau quadrature formulae associated with the spaces of parabolic splines with equidistant knots. These quadrature formulae are known to be asymptotically optimal in Sobolev spaces W p 3. Sharp estimates for the error constant in W ∞ 3 are given.

Physics::Computational PhysicsSobolev spaceAsymptotically optimal algorithmMathematical analysisGaussEquidistantConstant errorMathematics::Numerical AnalysisMathematicsQuadrature (mathematics)
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Shape design optimization in 2D aerodynamics using Genetic Algorithms on parallel computers

1996

Publisher Summary This chapter presents two Shape Optimization problems for two dimensional airfoil designs. The first one is a reconstruction problem for an airfoil when the velocity of the flow is known on the surface of airfoil. The second problem is to minimize the shock drag of an airfoil at transonic regime. The flow is modeled by the full potential equations. The discretization of the state equation is done using the finite element method and the resulting non-linear system of equations is solved by using a multi-grid method. The non-linear minimization process corresponding to the shape optimization problems are solved by a parallel implementation of a genetic algorithm (GA). Some n…

Physics::Fluid DynamicsAirfoilOptimal designMathematical optimizationDiscretizationApplied mathematicsShape optimizationAerodynamicsTransonicFinite element methodMathematicsSequential quadratic programming
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Optimal design for transonic flows

1991

The feasibility of finite element and mathematical programming methods for finding an optimal shape for an symmetric airfoil in case of transonic flow is studied. The state problem is solved using multigrid-technique. Numerical examples are given.

Physics::Fluid DynamicsAirfoilOptimal designMultigrid methodComputer scienceApplied mathematicsState (computer science)TransonicFinite element method
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APROS-NIMBUS: Dynamic Process Simulator and Interactive Multiobjective Optimization in Plant Automation

2013

Abstract Virtual commissioning of chemical plants often involves a dynamic simulator and an optimization method. This paper demonstrates the integration of APROS, a dynamic process simulator and IND-NIMBUS, an interactive multiobjective optimization software. We implement a multiobjective concentration control problem in APROS involving conflicting objectives and employ a decision maker to interact with IND-NIMBUS and express his preference information to finally obtain his most preferred solution. The results of this study show that APROS and IND-NIMBUS can be integrated and an interactive multiobjective optimization method can help the decision maker in exploring trade-offs among conflict…

Plant automationpareto optimalityComputer scienceProcess (engineering)business.industryControl (management)multiple criteria decision makingDecision makerMulti-objective optimizationdecision makingSoftwareConflicting objectivesbusinessSimulation
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New Results in Generalized Minimum Variance Control of Computer Networks

2014

In this paper new results in adaptive (generalized) minimum variance control of packet switching computer networks are presented. New solutions, corresponding to the new inverses of the nonsquare polynomial matrices, can be used for design of robust control of multivariable systems with different number of inputs and outputs. Application of polynomial matrix inverses with arbitrary degrees of freedom creates the possibilities to optimal control of computer networks in terms of usage their maximal bandwidth. Simulation examples made in Matlab environment show big potential of presented approach. DOI: http://dx.doi.org/10.5755/j01.itc.43.3.6268

PolynomialComputer sciencebusiness.industryMultivariable calculusDegrees of freedom (statistics)Optimal controlPolynomial matrixComputer Science ApplicationsMinimum-variance unbiased estimatorControl and Systems EngineeringElectrical and Electronic EngineeringRobust controlMATLABbusinesscomputercomputer.programming_languageComputer networkInformation Technology And Control
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Optimal Impulse Control Problems and Linear Programming

2009

Optimal impulse control problems are, in general, difficult to solve. A current research goal is to isolate those problems that lead to tractable solutions. In this paper, we identify a special class of optimal impulse control problems which are easy to solve. Easy to solve means that solution algorithms are polynomial in time and therefore suitable to the on-line implementation in real-time problems. We do this by using a paradigm borrowed from the Operations Research field. As main result, we present a solution algorithm that converges to the exact solution in polynomial time. Our approach consists in approximating the optimal impulse control problem via a binary linear programming proble…

PolynomialMathematical optimizationUnimodular matrixComputational complexity theoryLinear programmingbusiness.industryImpulse control hybrid systems optimal controlLocal search (optimization)Relaxation (approximation)Optimal controlbusinessTime complexityMathematics
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Optimal Control of the Lotka-Volterra Equations with Applications

2022

In this article, the Lotka-Volterra model is analyzed to reduce the infection of a complex microbiote. The problem is set as an optimal control problem, where controls are associated to antibiotic or probiotic agents, or transplantations and bactericides. Candidates as minimizers are selected using the Maximum Principle and the closed loop optimal solution is discussed. In particular a 2d-model is constructed with 4 parameters to compute the optimal synthesis using homotopies on the parameters.

Population dynamicsMaximum Principle[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC]Lotka-Volterra equations[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]Regular synthesisOptimal control
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A Decision Analysis Approach to Multiple-Choice Examinations

1998

We present a decision analysis approach to the problems faced by people subject to multiple-choice examinations, as often encountered in their education, in looking for a job, or in getting a driving permit.

Probability assessmentManagement scienceDecision treeSubject (documents)Decision ruleMathematicsOptimal decisionMultiple choiceDecision analysis
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The linear saturated decentralized strategy for constrained flow control is asymptotically optimal

2013

We present an algorithm for constrained network flow control in the presence of an unknown demand. Our algorithm is decentralized in the sense that it is implemented by a team of agents, each controlling just the flow on a single arc of the network based only on the buffer levels at the nodes at the extremes of the arc, while ignoring the actions of other agents and the network topology. We prove that our algorithm is also stabilizing and steady-state optimal. Specifically, we show that it asymptotically produces the minimum-norm flow. We finally generalize our algorithm to networks with a linear dynamics and we prove that certain least-square optimality properties still hold.

Production-distribution systemsOptimizationMathematical optimizationRobust controlUncertain systemsMinimum normNetwork topologyMinimum norm flowControl theoryElectric network topologyConstrained flowUncertain systemsElectrical and Electronic EngineeringMathematicsFlow control (data)Network topologyAsymptotically optimalRobust control; OptimizationUncertain systemEthernet flow controlAsymptotically optimal Constrained flow Distributed flow control Minimum norm Network optimization Network topology Production-distribution systems Steady-state optimal; Algorithms Electric network topology Flow control Uncertain systems; OptimizationProduction-distribution systemFlow controlAsymptotically optimal algorithmControl and Systems EngineeringSteady-state optimalMinimum-cost flow problemDistributed flow controlRobust controlNetwork optimization; Distributed flow control; Production-distribution systems; Uncertain systems; Minimum norm flowNetwork optimizationAlgorithms
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On Using the Theory of Regular Functions to Prove the ε-Optimality of the Continuous Pursuit Learning Automaton

2013

Published version of a chapter in the book: Recent Trends in Applied Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-38577-3_27 There are various families of Learning Automata (LA) such as Fixed Structure, Variable Structure, Discretized etc. Informally, if the environment is stationary, their ε-optimality is defined as their ability to converge to the optimal action with an arbitrarily large probability, if the learning parameter is sufficiently small/large. Of these LA families, Estimator Algorithms (EAs) are certainly the fastest, and within this family, the set of Pursuit algorithms have been considered to be the pioneering schemes. The…

Property (philosophy)Learning automataComputer scienceVDP::Mathematics and natural science: 400::Information and communication science: 420::Algorithms and computability theory: 422Structure (category theory)Monotonic functionMathematical proofAutomatonArbitrarily largeε-optimalityContinuous Pursuit AlgorithmCalculuspursuit algorithmsAlgorithmVariable (mathematics)
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