Topic
In-crowd algorithm
About: In-crowd algorithm is a research topic. Over the lifetime, 2 publications have been published within this topic receiving 136 citations.
Papers
TL;DR: This paper introduces InCrowd algorithm to matrix based ISD and uses FISTA to solve sub-problem at each In-Crowd update, and proposes an approach to further improve the speed of ISD or inversion denoising using the lateral continuity of seismic data.
Abstract: Seismic signal spectral decomposition can be solved as an inverse problem, known as Inverse Spectral Decomposition (ISD). It generates a higher time-frequency resolution spectrum but also takes a higher cost. ISD can be solved using matrix or operator iterations, the former of which requires much higher cost. In this paper, we introduce InCrowd algorithm to matrix based ISD. We use FISTA to solve sub-problem at each In-Crowd update. Proposed algorithm performs much faster even than operator based FISTA algorithm. Inverse denoising can be done in the same way by adjusting the inversion parameters. We also utilize an approach to further improve the speed of ISD or inversion denoising using the lateral continuity of seismic data. Synthetic examples are used to demonstrate the advantages of the proposed method.
3 citations
TL;DR: It is shown empirically that the in-crowd algorithm is faster than the best alternative solvers (homotopy, fixed point continuation and spectral projected gradient for l1 minimization) on certain well- and ill-conditioned sparse problems with more than 1000 unknowns.
Abstract: We introduce a fast method, the “in-crowd” algorithm, for finding the exact solution to basis pursuit denoising problems. The in-crowd algorithm discovers a sequence of subspaces guaranteed to arrive at the support set of the final solution of l1 -regularized least squares problems. We provide theorems showing that the in-crowd algorithm always converges to the correct global solution to basis pursuit denoising problems. We show empirically that the in-crowd algorithm is faster than the best alternative solvers (homotopy, fixed point continuation and spectral projected gradient for l1 minimization) on certain well- and ill-conditioned sparse problems with more than 1000 unknowns. We compare the in-crowd algorithm's performance in high- and low-noise regimes, demonstrate its performance on more dense problems, and derive expressions giving its computational complexity.
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2012 | 1 |
| 2011 | 1 |