Results 11 to 20 of about 8,591 (298)
Approximation Properties of the Vector Weak Rescaled Pure Greedy Algorithm
Mathematics, 2023 We first study the error performances of the Vector Weak Rescaled Pure Greedy Algorithm for simultaneous approximation with respect to a dictionary D in a Hilbert space.
Xu Xu +3 more
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Improving Greedy Algorithms for Rational Approximation
SIAM Journal on Scientific ComputingWhen developing robust preconditioners for multiphysics problems, fractional functions of the Laplace operator often arise and need to be inverted. Rational approximation in the uniform norm can be used to convert inverting those fractional operators into inverting a series of shifted Laplace operators.
James H. Adler +3 more
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Sharp conditions for the convergence of greedy expansions with prescribed coefficients
Open Mathematics, 2021 Greedy expansions with prescribed coefficients were introduced by V. N. Temlyakov in a general case of Banach spaces. In contrast to Fourier series expansions, in greedy expansions with prescribed coefficients, a sequence of coefficients {cn}n=1∞{\left\{{
Valiullin Artur R., Valiullin Albert R.
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A simple greedy approximation algorithm for the unit disk cover problem [PDF]
AUT Journal of Mathematics and Computing, 2020 Given a set $\mathcal P$ of $n$ points in the plane, the unit disk cover problem, which is known as an NP-hard problem, seeks to find the minimum number of unit disks that can cover all points of $\mathcal P$. We present a new $4$-approximation algorithm
Mahdi Imanparast, Seyed Naser Hashemi
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Demonstratio Mathematica, 2022 Generalized approximate weak greedy algorithms (gAWGAs) were introduced by Galatenko and Livshits as a generalization of approximate weak greedy algorithms, which, in turn, generalize weak greedy algorithm and thus pure greedy algorithm.
Valiullin Artur R. +2 more
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Sparse Approximation and Recovery by Greedy Algorithms [PDF]
IEEE Transactions on Information Theory, 2014 We study sparse approximation by greedy algorithms. Our contribution is two-fold. First, we prove exact recovery with high probability of random $K$-sparse signals within $\lceil K(1+\e)\rceil$ iterations of the Orthogonal Matching Pursuit (OMP). This result shows that in a probabilistic sense the OMP is almost optimal for exact recovery.
Eugene D. Livshitz +1 more
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Optimal Power Allocation With Multiple Joint Associations in Multi-User MIMO Full-Duplex Systems
IEEE Access, 2023 Optimum power allocation is an effective way to mitigate residual self-interference and inter-user interference in multiple input multiple output full-duplex (FD) systems.
Kunbei Pan, Bin Zhou, Zhiyong Bu
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Monotone Submodular Maximization over a Matroid via Non-Oblivious Local Search [PDF]
, 2013 We present an optimal, combinatorial 1−1/e approximation algorithm for monotone submodular optimization over a matroid constraint. Compared to the continuous greedy algorithm (Calinescu, Chekuri, Pál and Vondrák, 2008), our algorithm is extremely simple ...
Filmus, Yuval +3 more
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SPARSE APPROXIMATION AND RECOVERY BY GREEDY ALGORITHMS IN BANACH SPACES
Forum of Mathematics, Sigma, 2014 We study sparse approximation by greedy algorithms. We prove the Lebesgue-type inequalities for the weak Chebyshev greedy algorithm (WCGA), a generalization of the weak orthogonal matching pursuit to the case of a Banach space.
V. N. TEMLYAKOV
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Efficient Densest Subgraphs Discovery in Large Dynamic Graphs by Greedy Approximation
IEEE Access, 2023 Densest subgraph detection has become an important primitive in graph mining tasks when analyzing communities and detecting events in a wide range of application domains.
Tao Han
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