Polynomial elimination after weighting yields an explicit algebraic equation for the Pareto front in multi-objective polynomial optimization.
Globally optimal least-squares ARMA model identification is an eigenvalue problem.IEEE Control Systems Letters, 3(4):1062–1067
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Authors introduce backward error analysis, condition numbers, and pseudospectra for rectangular multispectral eigenvalue problems, noting that best-conditioned eigenvalues often align with globally optimal solutions in system identification examples.
MacaulayLab is a freely available Matlab toolbox that solves multivariate polynomial systems and rectangular multiparameter eigenvalue problems with one common numerical linear algebra approach independent of polynomial basis and monomial order while handling positive-dimensional solution sets at at
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Computing the Pareto Front by Polynomial Elimination, With an Application From System Identification
Polynomial elimination after weighting yields an explicit algebraic equation for the Pareto front in multi-objective polynomial optimization.
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Rectangular Multispectral Perturbation Theory
Authors introduce backward error analysis, condition numbers, and pseudospectra for rectangular multispectral eigenvalue problems, noting that best-conditioned eigenvalues often align with globally optimal solutions in system identification examples.
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Solving Multivariate Polynomial Systems and Rectangular Multiparameter Eigenvalue Problems with MacaulayLab
MacaulayLab is a freely available Matlab toolbox that solves multivariate polynomial systems and rectangular multiparameter eigenvalue problems with one common numerical linear algebra approach independent of polynomial basis and monomial order while handling positive-dimensional solution sets at at