Results 231 to 240 of about 87,394 (276)

Dynamic sensor selection for biomarker discovery. [PDF]

Proc Natl Acad Sci U S A
Pickard J, Stansbury C, Surana A, Muir L, Bloch A, Rajapakse I.   +5 more
europepmc   +1 more source

Enhancement of rime algorithm using quadratic interpolation learning for parameters identification of photovoltaic models. [PDF]

Sci Rep
Mohamed SA, Shaheen AM, Alqahtani MH, Al Faiya BM.   +3 more
europepmc   +1 more source

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Adaptive Greedy Approximations

Constructive Approximation, 1997
Let \({\mathcal H}\) be a Hilbert space. A dictionary \({\mathcal D}\) for \({\mathcal H}\) is a family of unit vectors in \({\mathcal H}\) such that finite linear combinations of \(g_{\gamma }\in {\mathcal D}\) are dense in \({\mathcal H}\). The problem of approximations over a dictionary is studied from different points.
Davis, G., Mallat, S., Avellaneda, M.
openaire   +3 more sources

Generalized Approximate Weak Greedy Algorithms

Mathematical Notes, 2005
The authors discuss so-called ``generalized approximate weak greedy algorithms'' (gAWGAs) in a Hilbert space, which describe the process of greedy expansions involving errors in calculation of the coefficients in terms of their absolut values. The concepts of ``dictionary'' in a real Hilbert space with inner product and of the ``gAWGA-expansion of an ...
Galatenko, V. V., Livshits, E. D.
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Greedy Approximations

Acta Numerica, 2006
In this survey we discuss properties of specific methods of approximation that belong to a family of greedy approximation methods (greedy algorithms). It is now well understood that we need to study nonlinear sparse representations in order to significantly increase our ability to process (compress, denoise,etc.) large data sets. Sparse representations
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Greedy approximation with regard to non-greedy bases

Advances in Computational Mathematics, 2010
The authors present the properties of basis which are important for certain direct and inverse theorems in nonlinear approximation. They study greedy approximation with regard to the basis with different properties. Some results known for unconditional bases are extended to the case of quasi-greedy bases.
Temlyakov, V. N., Yang, Mingrui, Ye, Peixin   +2 more
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Two Lower Estimates in Greedy Approximation

Constructive Approximation, 2003
Given an arbitrary Hilbert space with a denumerable orthonormal basis, the authors construct two examples providing lower estimates for the rate of convergence of the pure Greedy algorithm and of the weak Greedy algorithm, respectively.
Livshitz, E. D., Temlyakov, V. N.
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Relaxation in Greedy Approximation

Constructive Approximation, 2007
We study greedy algorithms in a Banach space from the point of view of convergence and rate of convergence. There are two well-studied approximation methods: the Weak Chebyshev Greedy Algorithm (WCGA) and the Weak Relaxed Greedy Algorithm (WRGA). The WRGA is simpler than the WCGA in the sense of computational complexity.
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Greedy approximation by arbitrary sets

Izvestiya: Mathematics, 2020
Abstract We define various algorithms for greedy approximations by elements of an arbitrary set in a Banach space. We study the convergence of these algorithms
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