Search results for " Computational"

showing 10 items of 661 documents

Robust stability and stabilization of uncertain T-S fuzzy systems with time-varying delay: An input-output approach

2013

An input-output approach to the stability and stabilization of uncertain Takagi-Sugeno (T-S) fuzzy systems with time-varying delay is proposed in this paper. The time-varying parameter uncertainties are assumed to be norm-bounded, and the delay is intervally time varying. A novel method is employed to approximate the time-varying delay, based on which the considered system is transformed into a feedback interconnection form. The new formulation of the system is comprised of a forward subsystem with constant time delay and a feedback subsystem embedding the uncertainties. By applying the scaled small-gain theorem to the converted system, less conservative stability and stabilization criteria…

Input/outputInterconnectionMathematical optimizationTakagi-Sugeno (T-S) modelApplied MathematicsUncertain systemsFuzzy control systemstabilityStability (probability)scaled small gain theoremComputational Theory and MathematicsControl theoryDelay systems; scaled small gain theorem; stability; Takagi-Sugeno (T-S) model; Control and Systems Engineering; Artificial Intelligence; Computational Theory and Mathematics; Applied MathematicsControl and Systems EngineeringArtificial IntelligenceEmbeddingRobust controlConstant (mathematics)MathematicsDelay systems
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Wannier90 as a community code: new features and applications

2019

Wannier90 is an open-source computer program for calculating maximally-localised Wannier functions (MLWFs) from a set of Bloch states. It is interfaced to many widely used electronic-structure codes thanks to its independence from the basis sets representing these Bloch states. In the past few years the development of Wannier90 has transitioned to a community-driven model; this has resulted in a number of new developments that have been recently released in Wannier90 v3.0. In this article we describe these new functionalities, that include the implementation of new features for wannierisation and disentanglement (symmetry-adapted Wannier functions, selectively-localised Wannier functions, s…

Interface (Java)02 engineering and technologysemiconductors01 natural sciencesGeneral Materials Sciencefieldslocal orbitalCondensed Matter - Materials ScienceUnit testingComputer programBasis (linear algebra)electronstooldynamicsComputational Physics (physics.comp-ph)021001 nanoscience & nanotechnologyCondensed Matter Physicsspin polarizationreal-space methods[PHYS.COND.CM-MS]Physics [physics]/Condensed Matter [cond-mat]/Materials Science [cond-mat.mtrl-sci]0210 nano-technologyPhysics - Computational PhysicspseudopotentialsconstructionMaterials sciencelocal orbitalsFluids & Plasmasreal-space method0204 Condensed Matter PhysicsFOS: Physical sciencesComputational sciencecrystalSet (abstract data type)band structure interpolation0103 physical sciencesddc:530Wannier function010306 general physics0912 Materials Engineeringdensity-functional theoryWannier orbitalWannier function1007 Nanotechnologybusiness.industrywannier orbitalsMaterials Science (cond-mat.mtrl-sci)Usabilitywannier functionsWannier functions; band structure interpolation; local orbitals; real-space methods; electronic structure; Wannier orbitals; density-functional theoryelectronic structureAutomationtotal-energy calculationsbusiness
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The inverse eigenvalue problem for a Hermitian reflexive matrix and the optimization problem

2016

The inverse eigenvalue problem and the associated optimal approximation problem for Hermitian reflexive matrices with respect to a normal {k+1}-potent matrix are considered. First, we study the existence of the solutions of the associated inverse eigenvalue problem and present an explicit form for them. Then, when such a solution exists, an expression for the solution to the corresponding optimal approximation problem is obtained.

Inverse iterationOptimization problemApplied Mathematics010102 general mathematicsMathematical analysisInverseGeneralized inversesEigenvalues010103 numerical & computational mathematicsExpression (computer science)Hermitian matrixMatrius (Matemàtica)01 natural sciencesHermitian matrixComputational MathematicsMatrix (mathematics)Applied mathematics0101 mathematicsDivide-and-conquer eigenvalue algorithmÀlgebra linealOptimization problemMATEMATICA APLICADAEigenvalues and eigenvectorsMathematics
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Minimal star-varieties of polynomial growth and bounded colength

2018

Abstract Let V be a variety of associative algebras with involution ⁎ over a field F of characteristic zero. Giambruno and Mishchenko proved in [6] that the ⁎-codimension sequence of V is polynomially bounded if and only if V does not contain the commutative algebra D = F ⊕ F , endowed with the exchange involution, and M , a suitable 4-dimensional subalgebra of the algebra of 4 × 4 upper triangular matrices , endowed with the reflection involution. As a consequence the algebras D and M generate the only varieties of almost polynomial growth. In [20] the authors completely classify all subvarieties and all minimal subvarieties of the varieties var ⁎ ( D ) and var ⁎ ( M ) . In this paper we e…

Involution (mathematics)Algebra and Number Theory010102 general mathematicsSubalgebraTriangular matrix010103 numerical & computational mathematics01 natural sciencesCombinatoricsSettore MAT/02 - Algebra*-colength *-codimension *-cocharacterBounded function0101 mathematicsCommutative algebraAssociative propertyMathematicsJournal of Pure and Applied Algebra
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Polynomial growth and star-varieties

2016

Abstract Let V be a variety of associative algebras with involution over a field F of characteristic zero and let c n ⁎ ( V ) , n = 1 , 2 , … , be its ⁎-codimension sequence. Such a sequence is polynomially bounded if and only if V does not contain the commutative algebra F ⊕ F , endowed with the exchange involution, and M, a suitable 4-dimensional subalgebra of the algebra of 4 × 4 upper triangular matrices. Such algebras generate the only varieties of ⁎-algebras of almost polynomial growth, i.e., varieties of exponential growth such that any proper subvariety is polynomially bounded. In this paper we completely classify all subvarieties of the ⁎-varieties of almost polynomial growth by gi…

Involution (mathematics)Algebra and Number TheorySubvariety010102 general mathematicsSubalgebraStar-codimensionTriangular matrixStar-polynomial identitie010103 numerical & computational mathematicsGrowth01 natural sciencesCombinatoricsSettore MAT/02 - AlgebraExponential growthBounded function0101 mathematicsCommutative algebraAssociative propertyMathematics
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Some characterizations of algebras with involution with polynomial growth of their codimensions

2018

Let A be an associative algebra endowed with an involution ∗ of the first kind and let c ∗n (A) denote the sequence of ∗-codimensions of A. In this paper, we are interested in algebras with involution such that the ∗-codimension sequence is polynomially bounded. We shall prove that A is of this kind if and only if it satisfies the same identities of a finite direct sum of finite dimensional algebras with involution A i , each of which with Jacobson radical of codimension less than or equal to one in A i . We shall also relate the condition of having polynomial codimension growth with the sequence of cocharacters and with the sequence of colengths. Along the way, we shall show that the multi…

Involution (mathematics)polynomial growthAlgebra and Number Theory16R50010102 general mathematicsSecondary: 16R10010103 numerical & computational mathematics01 natural sciencesPolynomial identitiesCombinatoricsPrimary: 16W10Polynomial identitieAssociative algebraAlgebras with involution0101 mathematics16R50; algebras with involution; polynomial growth; Polynomial identities; Primary: 16W10; Secondary: 16R10Mathematics
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The smoothed particle hydrodynamics method via residual iteration

2019

Abstract In this paper we propose for the first time an iterative approach of the Smoothed Particle Hydrodynamics (SPH) method. The method is widespread in many areas of science and engineering and despite its extensive application it suffers from several drawbacks due to inaccurate approximation at boundaries and at irregular interior regions. The presented iterative process improves the accuracy of the standard method by updating the initial estimates iterating on the residuals. It is appealing preserving the matrix-free nature of the method and avoiding to modify the kernel function . Moreover the process refines the SPH estimates and it is not affected by disordered data distribution. W…

Iterative and incremental developmentComputer scienceMechanical EngineeringComputational MechanicsProcess (computing)General Physics and Astronomy010103 numerical & computational mathematicsBivariate analysisIterated residualResidual01 natural sciencesComputer Science Applications010101 applied mathematicsSmoothed-particle hydrodynamicsSettore MAT/08 - Analisi NumericaDistribution (mathematics)Smoothed particle hydrodynamicMechanics of MaterialsConvergence (routing)Test functions for optimization0101 mathematicsConvergenceAlgorithmAccuracyKernel based method
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Dynamique de la structure industrielle française

1990

JEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C67 - Input–Output ModelsJEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation ModelingJEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C67 - Input–Output ModelsJEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C61 - Optimization Techniques • Programming Models • Dynamic AnalysisJEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation ModelingJEL: L - Industrial Organization/L.L1 - Market Structure Firm Strategy and Market Performance/L.L1.L16 - Industrial Organization and Macroeconomics: Industrial Structure and Structural Change • Industrial Price IndicesJEL : D - Microeconomics/D.D5 - General Equilibrium and Disequilibrium/D.D5.D57 - Input–Output Tables and AnalysisJEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C61 - Optimization Techniques • Programming Models • Dynamic Analysis[ SHS.ECO ] Humanities and Social Sciences/Economies and financesJEL: D - Microeconomics/D.D5 - General Equilibrium and Disequilibrium/D.D5.D57 - Input–Output Tables and AnalysisJEL : L - Industrial Organization/L.L1 - Market Structure Firm Strategy and Market Performance/L.L1.L16 - Industrial Organization and Macroeconomics: Industrial Structure and Structural Change • Industrial Price Indices[SHS.ECO]Humanities and Social Sciences/Economics and Finance[SHS.ECO] Humanities and Social Sciences/Economics and Finance
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A Note on added information in the RAS Procedure: reexamination of some evidence

2006

International audience; An example in Miernyk (1977) presented a rather counterintuitive result, namely that introducing accurate exogenous information into an RAS matrix estimating procedure could lead to an estimate that was worse than one generated by RAS using no exogenous information at all. This became an oft-cited black mark against RAS. Miller and Blair (1985) included a different (and small) illustration of the same possibility. It was recently pointed out by one of us that the Miller/Blair numerical results are wrong. For that reason, we decided to reexamine all the empirical evidence we could find on the subject. While figures in both Miernyk and Miller/Blair appear to be wrong, …

JEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C67 - Input–Output ModelsJEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C67 - Input–Output ModelsCounterintuitiveClosenessJEL: D - Microeconomics/D.D5 - General Equilibrium and Disequilibrium/D.D5.D57 - Input–Output Tables and AnalysisEnvironmental Science (miscellaneous)Development[SHS.ECO]Humanities and Social Sciences/Economics and FinanceJEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation ModelingJEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation ModelingInput-outputbiproportionEconometricsJEL : D - Microeconomics/D.D5 - General Equilibrium and Disequilibrium/D.D5.D57 - Input–Output Tables and Analysis[ SHS.ECO ] Humanities and Social Sciences/Economies and finances[SHS.ECO] Humanities and Social Sciences/Economics and FinanceEmpirical evidenceMathematical economicsCounterexampleMathematicsRAS
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Normalizing biproportional methods

2002

International audience; Biproportional methods are used to update matrices: the projection of a matrix Z to give it the column and row sums of another matrix is R Z S, where R and S are diagonal and secure the constraints of the problem (R and S have no signification at all because they are not identified). However, normalizing R or S generates important mathematical difficulties: it amounts to put constraints on Lagrange multipliers, non negativity (and so the existence of the solution) is not guaranteed at equilibrium or along the path to equilibrium.

JEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C67 - Input–Output Modelsjel:C63Diagonaljel:C67JEL: D - Microeconomics/D.D5 - General Equilibrium and Disequilibrium/D.D5.D57 - Input–Output Tables and Analysismathematical economicsColumn (database)Projection (linear algebra)Combinatoricssymbols.namesakeMatrix (mathematics)JEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation ModelingmatricesJEL : D - Microeconomics/D.D5 - General Equilibrium and Disequilibrium/D.D5.D57 - Input–Output Tables and Analysis[ SHS.ECO ] Humanities and Social Sciences/Economies and financesNon negativity[SHS.ECO] Humanities and Social Sciences/Economics and FinanceGeneral Environmental ScienceMathematicsJEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C67 - Input–Output ModelsGeneral Social Sciences[SHS.ECO]Humanities and Social Sciences/Economics and Financejel:D57community developmentJEL : C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation ModelingLagrange multiplierPath (graph theory)symbols
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