greedy algorithmpure greedy algorithmrate of convergence of greedy algorithmsHilbert spacedifference-differential inequalitiesThis paper is devoted to the study of the rate of convergence of pure greedy algorithms in Hilbert space. We obtain upper bounds for the rate of convergence of pure greedy ...
which they solve using the OMP [22]. Based on the sparse estimate of the CA, they propose two detection methods that exploit different CA properties. The first one,
We obtain upper bounds for the rate of convergence of pure greedy algorithms for functions from the class A α, β(D).doi:10.1023/B:MATN.0000043480.05267.e7E. D. LivshitsMathematical NotesE. D. Livshits.Rate of Convergence of Pure Greedy Algorithms[J]. Mathematical Notes .2004(3-4)...
Comparison of the convergence rate of pure greedy and orthogonal greedy algorithmsDereventsovA. V.MATHEMATICAL NOTES -NEW YORK- C/C OF MATEMATICHESKIE ZAMETIK
pure greedy algorithmrate of convergence of greedy algorithmsHilbert spacedifference-differential inequalitiesThis paper deals with the approximation of elements of a real separable Hilbert space H with inner product (·,·). A subset of elements D is contained in H is called a dictionary if ‖g...
the orthogonal greedy algorithm is optimal and significantly exceeds the pure greedy algorithm. The main result in the present paper is the assertion that the situation can also be opposite for separate elements of the class () (and even of the class ()): the rate of convergence of the ...
Our new results provide better bounds for the accuracy than known results in the case of small ∥𝑓∥. Keywords: greedy approximation; rate of convergence; pure greedy algorithm MSC: 41A251. Introduction This paper is devoted to the theoretical study of the efficiency of some greedy algorithms...
If the structural design is reasonable, the results of the algorithm will tend to converge with the training process of the agent. In this section, the convergence of the algorithm is demonstrated from two aspects: RL and improved 𝜀ε-greedy strategy. 2.2.1. Reinforcement Learning RL is a...
They, however, did not consider the effect of the algorithm on energy efficiency. In Ref. [20], dynamic LoRa (DyLoRa) was proposed, a scheme that uses a symbol error rate model to determine an energy efficient SF and TP allocation. Optimizing convergence time of the ADR mechanism is ...
Plug-and-play ADMM for image restoration: Fixed-point convergence and applications. IEEE Trans. Comput. Imaging 2016, 3, 84–98. [Google Scholar] [CrossRef] Prono, L.; Mangia, M.; Marchioni, A.; Pareschi, F.; Rovatti, R.; Setti, G. Deep Neural Oracle With Support Identification in ...