Search results for "Linear"

showing 10 items of 7165 documents

Polynomial numerical indices of 𝐶(𝐾) and 𝐿₁(𝜇)

2013

We estimate the polynomial numerical indices of the spaces C ( K ) C(K) and L 1 ( μ ) L_1(\mu ) .

Reciprocal polynomialAlternating polynomialStable polynomialMinimal polynomial (linear algebra)Applied MathematicsGeneral MathematicsApplied mathematicsMonic polynomialWilkinson's polynomialMathematicsCharacteristic polynomialMatrix polynomialProceedings of the American Mathematical Society
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Inequalities for Information Potentials and Entropies

2020

We consider a probability distribution p0(x),p1(x),&hellip

Recurrence relationprobability distributionGeneral MathematicsTsallis entropylcsh:Mathematics010102 general mathematicsLinear operatorsfunctional equationslcsh:QA1-93901 natural sciencesinformation potentialRényi entropyCombinatorics010104 statistics & probabilityRényi entropyinequalitiesComputer Science (miscellaneous)Order (group theory)Probability distribution0101 mathematicsTsallis entropyEngineering (miscellaneous)MathematicsMathematics
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Non-linear RLS-based algorithm for pattern classification

2006

A new non-linear recursive least squares (RLS) algorithm is presented in the context of pattern classification problems. The algorithm incorporates the non-linearity of the filter's output in the updating rules of the classical RLS algorithm. The proposed method yields lower stationary error levels when compared to the standard LMS and RLS algorithms in a classical application of pattern classification, such as the channel equalization problem.

Recursive least squares filterSignal processingEqualizationContext (language use)Filter (signal processing)Computer Science::OtherNonlinear systemComputer Science::SoundControl and Systems EngineeringSignal ProcessingComputer Vision and Pattern RecognitionElectrical and Electronic EngineeringAlgorithmSoftwareMathematicsSignal Processing
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A parallel radix-4 block cyclic reduction algorithm

2013

SUMMARY A conventional block cyclic reduction algorithm operates by halving the size of the linear system at each reduction step, that is, the algorithm is a radix-2 method. An algorithm analogous to the block cyclic reduction known as the radix-q partial solution variant of the cyclic reduction (PSCR) method allows the use of higher radix numbers and is thus more suitable for parallel architectures as it requires fever reduction steps. This paper presents an alternative and more intuitive way of deriving a radix-4 block cyclic reduction method for systems with a coefficient matrix of the form tridiag{ − I,D, − I}. This is performed by modifying an existing radix-2 block cyclic reduction me…

Reduction (complexity)Algebra and Number TheoryApplied MathematicsLinear systemPartial solutionRadixCoefficient matrixPartial fraction decompositionAlgorithmMathematicsBlock (data storage)Cyclic reductionNumerical Linear Algebra with Applications
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Calculation of excitation energies from the CC2 linear response theory using Cholesky decomposition

2014

A new implementation of the approximate coupled cluster singles and doubles CC2 linear response model is reported. It employs a Cholesky decomposition of the two-electron integrals that significantly reduces the computational cost and the storage requirements of the method compared to standard implementations. Our algorithm also exploits a partitioning form of the CC2 equations which reduces the dimension of the problem and avoids the storage of doubles amplitudes. We present calculation of excitation energies of benzene using a hierarchy of basis sets and compare the results with conventional CC2 calculations. The reduction of the scaling is evaluated as well as the effect of the Cholesky …

Reduction (complexity)Coupled clusterDimension (vector space)Basis (linear algebra)ChemistryComputational chemistryExtrapolationGeneral Physics and AstronomyApplied mathematicsPhysical and Theoretical ChemistryScalingBasis setCholesky decomposition
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On the intrinsic complexity of learning

1995

A new view of learning is presented. The basis of this view is a natural notion of reduction. We prove completeness and relative difficulty results. An infinite hierarchy of intrinsically more and more difficult to learn concepts is presented. Our results indicate that the complexity notion captured by our new notion of reduction differs dramatically from the traditional studies of the complexity of the algorithms performing learning tasks.

Reduction (complexity)HierarchyTheoretical computer scienceBasis (linear algebra)Computer scienceCompleteness (order theory)Recursive functionsRecursive operatorNatural (music)Inductive reasoning
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Energy-weighted M1 sum rule with explicit δ degrees of freedom

1985

Abstract The influence of Δ degrees of freedom on the energy-weighted M1 sum rule is investigated and applied to 2 H and 4 He. Using π- and ρ-exchange potentials a reduction of the potential contribution of the order of 50% is obtained. The absolute value of the sum rule is strongly dependent on the short-range behaviour of the nuclear wave function. Furthermore, the contribution of c.m. effects is evaluated and found to be of the order of 5–10%.

Reduction (complexity)PhysicsNuclear and High Energy PhysicsLinearity of differentiationRule of sumDegrees of freedomMathematical analysisOrder (group theory)Sum rule in quantum mechanicsAbsolute value (algebra)Wave functionNuclear Physics A
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Artificial Ground Motions and Nonlinear Response of RC Structures

2020

The selection of seismic inputs for nonlinear dynamic analysis is widely debated, mainly focusing on the advantages and disadvantages provided by the choice of natural, simulated, or artificial records. This work proves the differences in the structural behavior of RC buildings when using accelerograms with different levels of stationarity. Initially, nonlinear response under three sets of accelerograms equivalent in terms of pseudo acceleration spectrum is evaluated and compared. Then, the results of incremental dynamic analyses are compared by the statistical point of view considering different levels of irregularity for the reference structure.

Reference structureArticle Subjectbusiness.industryComputer scienceGround motion seismic input RC structures010401 analytical chemistryWork (physics)0211 other engineering and technologies02 engineering and technologyStructural engineeringEngineering (General). Civil engineering (General)01 natural sciences0104 chemical sciencesAccelerationNonlinear systemSettore ICAR/09 - Tecnica Delle Costruzioni021105 building & constructionPoint (geometry)TA1-2040businessCivil and Structural Engineering
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Self-calibration of a PTZ Camera Using New LMI Constraints

2013

In this paper, we propose a very reliable and flexible method for self-calibrating rotating and zooming cameras - generally referred to as PTZ (Pan-Tilt-Zoom) cameras. The proposed method employs a Linear Matrix Inequality (LMI) resolution approach and allows extra tunable constraints on the intrinsic parameters to be taken into account during the process of estimating these parameters. Furthermore, the considered constraints are simultaneously enforced in all views rather than in a single reference view. The results of our experiments show that the proposed approach allows for significant improvement in terms of accuracy and robustness when compared against state of the art methods.

Reference viewComputer scienceRobustness (computer science)Control theoryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPrincipal pointLinear matrix inequalityZoomCamera resectioning
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Fitting linear models and generalized linear models with large data sets in R

2009

We present an estimating algorithm to fit linear and generalized linear models not involving the QR decomposition. Some new R functions are presented and discussed. For large data sets, comparisons with respect to the well-known lm() and glm(), as well as to biglm() and bigglm() from the package biglm, show that the proposed functions speed up computation while preserving numerical stability and accuracy

Regression updating methodology and algorithms of statistical computing linear regression generalized linear regression statistical computing R programmingSettore SECS-S/01 - Statistica
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