Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Andreas M. Tillmann"'
Publikováno v:
Discrete Applied Mathematics. 283:346-366
We investigate conditions for the unique recoverability of sparse integer-valued signals from a small number of linear measurements. Both the objective of minimizing the number of nonzero components, the so-called l 0 -norm, as well as its popular su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a4005d943f297df4e2df1fee37fb0209
https://doi.org/10.1016/j.dam.2020.01.021
https://doi.org/10.1016/j.dam.2020.01.021
Autor:
Andreas M. Tillmann
Publikováno v:
Computational Optimization and Applications. 74:387-441
We investigate the NP-hard problem of computing the spark of a matrix (i.e., the smallest number of linearly dependent columns), a key parameter in compressed sensing and sparse signal recovery. To that end, we identify polynomially solvable special
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cbbc8a3b87eec1650ed0a471dc62868a
https://doi.org/10.1007/s10589-019-00114-9
https://doi.org/10.1007/s10589-019-00114-9
Publikováno v:
ICASSP
We propose a new formulation for the joint phase retrieval and dictionary learning problem with a reduced number of regularization parameters to be tuned. A parallel algorithm based on the block successive convex approximation framework is developed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::df93245d705d58ef01f61e489a35feef
https://doi.org/10.1109/icassp39728.2021.9413991
https://doi.org/10.1109/icassp39728.2021.9413991
Phase retrieval aims at reconstructing unknown signals from magnitude measurements of linear mixtures. In this paper, we consider the phase retrieval with dictionary learning problem, which includes an additional prior information that the measured s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8065afff2d14d70bc5b96ba67c12d5fb
Autor:
Christian Puchert, Andreas M. Tillmann
In recent years, several integer programming (IP) approaches were developed for maximum-likelihood decoding and minimum distance computation for binary linear codes. Two aspects in particular have been demonstrated to improve the performance of IP so
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::584cb72cb5d4d346399999e4f8243942
A primal-dual homotopy algorithm for $$\ell _{1}$$-minimization with $$\ell _{\infty }$$-constraints
Publikováno v:
Computational Optimization and Applications. 70:443-478
In this paper we propose a primal-dual homotopy method for $$\ell _1$$ -minimization problems with infinity norm constraints in the context of sparse reconstruction. The natural homotopy parameter is the value of the bound for the constraints and we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::688c771ab4abf4c99149cf4cc6a83be3
https://doi.org/10.1007/s10589-018-9983-4
https://doi.org/10.1007/s10589-018-9983-4
Autor:
Leif Kobbelt, Andreas M. Tillmann
We consider the problem of approximating a two-dimensional shape contour (or curve segment) using discrete assembly systems, which allow to build geometric structures based on limited sets of node and edge types subject to edge length and orientation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3846e3ce1ffe50d24e22465794bcf875
http://arxiv.org/abs/1909.09217
http://arxiv.org/abs/1909.09217
Autor:
Andreas M. Tillmann
Publikováno v:
PAMM. 15:735-738
In this note, we show that linear programming and the prominent Basis Pursuit problem (i.e., minimizing the l1-norm of a vector x subject to an underdetermined linear equation system Ax = b) are theoretically equivalent, and briefly discuss possible
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::64d4ca9ade5ab52171a2c32b52902659
https://doi.org/10.1002/pamm.201510351
https://doi.org/10.1002/pamm.201510351
Publikováno v:
ACM Transactions on Mathematical Software. 41:1-29
The problem of finding a minimum ℓ 1 -norm solution to an underdetermined linear system is an important problem in compressed sensing, where it is also known as basis pursuit . We propose a heuristic optimality check as a general tool for ℓ 1 -mi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::942dee5627ccb0eb81d1cfcf55f4f048
https://doi.org/10.1145/2689662
https://doi.org/10.1145/2689662
Publikováno v:
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2016, 64 (24), pp.6485-6500. ⟨10.1109/TSP.2016.2607180⟩
IEEE Transactions on Signal Processing, 2016, 64 (24), pp.6485-6500. ⟨10.1109/TSP.2016.2607180⟩
IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2016, 64 (24), pp.6485-6500. ⟨10.1109/TSP.2016.2607180⟩
IEEE Transactions on Signal Processing, 2016, 64 (24), pp.6485-6500. ⟨10.1109/TSP.2016.2607180⟩
author's preprint version; International audience; We propose a new algorithm to learn a dictionary for reconstructing and sparsely encoding signals from measurements without phase. Specifically, we consider the task of estimating a two-dimensional i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1c4bd19714563ac607f86afc92ede87f
https://hal.inria.fr/hal-01387428/document
https://hal.inria.fr/hal-01387428/document