Results 1 to 10 of about 29,667 (117)
The Spectrum of a Selfadjoint Compression of a Selfadjoint Operator [PDF]
Proceedings of the American Mathematical Society, 1970 The relation between the spectrum of a selfadjoint operator and the spectrum of its compression is investigated. In particular, we show that the compression of a selfadjoint operator is essentially selfadjoint if and only if the spectrum of the closure of the compression is contained in the closed convex hull of the spectrum of the operator.
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The compression of a slant Hankel operator to H2
Publications De L'Institut Mathematique, 2003 A slant Hankel operator K? with symbol ? in L?(T) (in short L?), where T is the unit circle on the complex plane, is an operator whose representing matrix M = (aij) is given by ai,j = (?,z-2i-j), where (?, ?) is the usual inner product in L2(T) (in short L2). The operator L? denotes the compression of K? to H2(T) (in short H2).
Zegeye, Taddesse, Arora, S. C.
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Multi-Objective Operator for Optimal Compression and De-compression of Random Signals
2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), 2018 New multi-objective operators of random signals are presented in this paper. The new operators improve, under a unrestrictive condition, the performance of known techniques: the generalized Karhunen-Loeve transform, the transform considered by Brillinger and the generalized Brillinger-like transform.
Pablo Soto-Quiros, Anatoli Torokhti
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Compressions of Multiplication Operators and Their Characterizations [PDF]
Results in Mathematics, 2020 AbstractDual truncated Toeplitz operators and other restrictions of the multiplication by the independent variable $$M_z$$ M z on the classical $$L^2$$ L 2 space on the unit circle are investigated. Commutators are calculated and commutativity is characterized.
M. Cristina Câmara +3 more
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Sparse Compression of Expected Solution Operators [PDF]
SIAM Journal on Numerical Analysis, 2020 We show that the expected solution operator of prototypical linear elliptic partial differential operators with random coefficients is well approximated by a computable sparse matrix. This result is based on a random localized orthogonal multiresolution decomposition of the solution space that allows both the sparse approximate inversion of the random ...
Michael Feischl, Daniel Peterseim
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Mixed operators in compressed sensing [PDF]
2010 44th Annual Conference on Information Sciences and Systems (CISS), 2010 Applications of compressed sensing motivate the possibility of using different operators to encode and decode a signal of interest. Since it is clear that the operators cannot be too different, we can view the discrepancy between the two matrices as a perturbation.
Matthew A. Herman, Deanna Needell
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Compressive identification of linear operators [PDF]
2011 IEEE International Symposium on Information Theory Proceedings, 2011 To be presented at IEEE Int. Symp. Inf.
Reinhard Heckel, Helmut Bölcskei
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Operator compression with deep neural networks
Advances in Continuous and Discrete Models, 2022 AbstractThis paper studies the compression of partial differential operators using neural networks. We consider a family of operators, parameterized by a potentially high-dimensional space of coefficients that may vary on a large range of scales. Based on the existing methods that compress such a multiscale operator to a finite-dimensional sparse ...
Kröpfl, Fabian +2 more
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LIE operators for compressive sensing [PDF]
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 We consider the efficient acquisition, parameter estimation, and recovery of signal ensembles that lie on a low-dimensional manifold in a high-dimensional ambient signal space. Our particular focus is on randomized, compressive acquisition of signals from the manifold generated by the transformation of a base signal by operators from a Lie group.
Chinmay Hegde +2 more
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Compression of quantum-measurement operations [PDF]
Physical Review A, 2001 We generalize recent work of Massar and Popescu dealing with the amount of classical data that is produced by a quantum measurement on a quantum state ensemble. In the previous work it was shown how spurious randomness generally contained in the outcomes can be eliminated without decreasing the amount of knowledge, to achieve an amount of data equal to
Winter, Andreas, Massar, Serge
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