Search results for "subspace"

showing 10 items of 164 documents

Approximation of Feasible Parameter Set in worst case identification of block-oriented nonlinear models

2003

Abstract The estimation of the Feasible Parameter Set for block-oriented nonlinear models in a worst case setting is considered. A bounding procedure is determined both for polytopic and ellipsoidie sets, consisting in the projection of the FPS ⊂ R MN of the extended parameter vector onto suitable M or N-dimensional subspaces and in the solution of convex optimization problems which provide the extreme points of the Parameter Uncertainties Intervals of the model parameteres. Bounds obtained are tighter then in the previous approaches.

Set (abstract data type)Nonlinear systemMathematical optimizationBounding overwatchConvex optimizationApplied mathematicsExtreme pointLinear subspaceProjection (linear algebra)MathematicsBlock (data storage)
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MR3785684 Reviewed Liu, Ai Fang(PRC-NAA); Li, Peng Tong(PRC-NAA) K-fusion frames and the corresponding generators for unitary systems. (English summa…

2018

Given an operator K∈B(H), in this paper the authors introduce K-fusion frames as a generalization of fusion frames, defined as a sequence {Wi} of closed subspaces of the Hilbert space H. For K=1, this becomes a fusion frame. They prove some properties of K-fusion frames, based on considering the operator K and the frame operators. In the last section, for a unitary system U, the authors define a K-fusion frame generator in a way similar to the frame vectors for unitary systems. Some properties and characterization theorems are derived for operators in the generalized local commutant of U.

Settore MAT/05 - Analisi MatematicaK-fusion frame K-fusion frame generator wandering subspace unitary system
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High-order regularization in lattice-Boltzmann equations

2017

A lattice-Boltzmann equation (LBE) is the discrete counterpart of a continuous kinetic model. It can be derived using a Hermite polynomial expansion for the velocity distribution function. Since LBEs are characterized by discrete, finite representations of the microscopic velocity space, the expansion must be truncated and the appropriate order of truncation depends on the hydrodynamic problem under investigation. Here we consider a particular truncation where the non-equilibrium distribution is expanded on a par with the equilibrium distribution, except that the diffusive parts of high-order nonequilibrium moments are filtered, i.e., only the corresponding advective parts are retained afte…

Shock waverecurrence relationspolynomialsComputational MechanicsLattice Boltzmann methods114 Physical sciences01 natural sciences010305 fluids & plasmassubspaces0103 physical sciences010306 general physicsFluid Flow and Transfer ProcessesPhysicstensor methods: shock tubesHermite polynomialsRecurrence relationta114AdvectionMechanical EngineeringpolynomitMathematical analysisCondensed Matter PhysicsDistribution functionMechanics of MaterialsRegularization (physics)shock tubes [tensor methods]Shear flowPhysics of Fluids
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Design of Asymmetric Shift Operators for Efficient Decentralized Subspace Projection

2021

A large number of applications in decentralized signal processing includes projecting a vector of noisy observations onto a subspace dictated by prior information about the field being monitored. Accomplishing such a task in a centralized fashion in networks is prone to a number of issues such as large power consumption, congestion at certain nodes and suffers from robustness issues against possible node failures. Decentralized subspace projection is an alternative method to address those issues. Recently, it has been shown that graph filters (GFs) can be implemented to perform decentralized subspace projection. However, most of the existing methods have focused on designing GFs for symmetr…

Signal processingOptimization problemComputer science020206 networking & telecommunications02 engineering and technologyShift operatorTopologyNetwork topologyGraphProjection (linear algebra)Operator (computer programming)Robustness (computer science)Signal Processing0202 electrical engineering electronic engineering information engineeringElectrical and Electronic EngineeringWireless sensor networkSubspace topologyIEEE Transactions on Signal Processing
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Robust subspace DOA estimation for wireless communications

2002

This paper is concerned with array signal processing in non-Gaussian noise typical in urban and indoor radio channels. Robust and fully nonparametric high resolution algorithms for direction of arrival (DOA) estimation are presented. The algorithms are based on multivariate spatial sign and rank concepts. The performance of the algorithms is studied using simulations. The results show that almost optimal performance is obtained in wide variety of noise conditions.

Signal processingbusiness.industryNoise (signal processing)Covariance matrixElectronic engineeringNonparametric statisticsWirelessDirection of arrivalbusinessAlgorithmSubspace topologyMathematicsMatrix decompositionVTC2000-Spring. 2000 IEEE 51st Vehicular Technology Conference Proceedings (Cat. No.00CH37026)
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APPROXIMATION OF BANACH SPACE VALUED NON-ABSOLUTELY INTEGRABLE FUNCTIONS BY STEP FUNCTIONS

2008

AbstractThe approximation of Banach space valued non-absolutely integrable functions by step functions is studied. It is proved that a Henstock integrable function can be approximated by a sequence of step functions in the Alexiewicz norm, while a Henstock–Kurzweil–Pettis and a Denjoy–Khintchine–Pettis integrable function can be only scalarly approximated in the Alexiewicz norm by a sequence of step functions. In case of Henstock–Kurzweil–Pettis and Denjoy–Khintchine–Pettis integrals the full approximation can be done if and only if the range of the integral is norm relatively compact.

Sobolev spacePure mathematicsRelatively compact subspaceIntegrable systemGeneral MathematicsNorm (mathematics)Step functionMathematical analysisBounded variationBanach spaceLocally integrable functionMathematicsGlasgow Mathematical Journal
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The Tan 2Θ Theorem in fluid dynamics

2017

We show that the generalized Reynolds number (in fluid dynamics) introduced by Ladyzhenskaya is closely related to the rotation of the positive spectral subspace of the Stokes block-operator in the underlying Hilbert space. We also explicitly evaluate the bottom of the negative spectrum of the Stokes operator and prove a sharp inequality relating the distance from the bottom of its spectrum to the origin and the length of the first positive gap.

Spectral subspacePhysics35Q35 47A67 (Primary) 35Q30 47A12 (Secondary)Spectrum (functional analysis)Mathematical analysisHilbert spaceReynolds numberStatistical and Nonlinear PhysicsMathematics - Spectral TheoryMathematics - Functional AnalysisPhysics::Fluid Dynamicssymbols.namesakeFluid dynamicssymbolsGeometry and TopologyStokes operatorNavier–Stokes equation ; Stokes operator ; Reynolds number ; rotation of subspaces ; quadratic forms ; quadratic numerical rangeRotation (mathematics)Mathematical Physics
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Quantum Walk Search with Time-Reversal Symmetry Breaking

2015

We formulate Grover's unstructured search algorithm as a chiral quantum walk, where transitioning in one direction has a phase conjugate to transitioning in the opposite direction. For small phases, this breaking of time-reversal symmetry is too small to significantly affect the evolution: the system still approximately evolves in its ground and first excited states, rotating to the marked vertex in time $\pi \sqrt{N} / 2$. Increasing the phase does not change the runtime, but rather changes the support for the 2D subspace, so the system evolves in its first and second excited states, or its second and third excited states, and so forth. Apart from the critical phases corresponding to these…

Statistics and ProbabilityPhysicsQuantum PhysicsGeneral Physics and AstronomyFOS: Physical sciencesStatistical and Nonlinear PhysicsQuantum searchVertex (geometry)T-symmetrySearch algorithmModeling and SimulationExcited stateQuantum mechanicsQuantum walkSymmetry breakingQuantum Physics (quant-ph)Mathematical PhysicsSubspace topology
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Spin-1/2 sub-dynamics nested in the quantum dynamics of two coupled qutrits

2017

In this paper we investigate the quantum dynamics of two spin-1 systems, $\vec{\textbf{S}}_1$ and $\vec{\textbf{S}}_2$, adopting a generalized $(\vec{\textbf{S}}_1+\vec{\textbf{S}}_2)^2$-nonconserving Heisenberg model. We show that, due to its symmetry property, the nine-dimensional dynamics of the two qutrits exactly decouples into the direct sum of two sub-dynamics living in two orthogonal four- and five-dimensional subspaces. Such a reduction is further strengthened by our central result consisting in the fact that in the four-dimensional dynamically invariant subspace, the two qutrits quantum dynamics, with no approximations, is equivalent to that of two non interacting spin 1/2's. The …

Statistics and ProbabilityQuantum dynamicsGeneral Physics and AstronomyFOS: Physical sciencesquantum mechanicquantum entanglement01 natural sciencesSettore FIS/03 - Fisica Della Materia010305 fluids & plasmasReduction (complexity)Theoretical physicsPhysics and Astronomy (all)0103 physical sciencesMathematical Physic010306 general physicsMathematical PhysicsSpin-½symmetry-based emergence of qubit subdynamicPhysicsQuantum PhysicsDirect sumHeisenberg modeltwo coupled qutrit Hamiltonian modelInvariant subspaceStatistical and Nonlinear PhysicsLinear subspaceSymmetry (physics)Modeling and SimulationQuantum Physics (quant-ph)Statistical and Nonlinear Physic
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Experiments in Value Function Approximation with Sparse Support Vector Regression

2004

We present first experiments using Support Vector Regression as function approximator for an on-line, sarsa-like reinforcement learner. To overcome the batch nature of SVR two ideas are employed. The first is sparse greedy approximation: the data is projected onto the subspace spanned by only a small subset of the original data (in feature space). This subset can be built up in an on-line fashion. Second, we use the sparsified data to solve a reduced quadratic problem, where the number of variables is independent of the total number of training samples seen. The feasability of this approach is demonstrated on two common toy-problems.

Support vector machineFunction approximationVariablesmedia_common.quotation_subjectFeature vectorReinforcement learningFunction (mathematics)AlgorithmSubspace topologyVector spaceMathematicsmedia_common
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