Optimal $k$-Thresholding Algorithms for Sparse Optimization Problems
نویسندگان
چکیده
منابع مشابه
Parallel Local Elimination Algorithms for Sparse Discrete Optimization Problems
The development and study of a parallel implementation of the graph-based local elimination algorithms on novel emergent parallel GPU-based architectures for solving sparse discrete optimization (DO) problems are discussed.
متن کاملBlock local elimination algorithms for solving sparse discrete optimization problems
Block elimination algorithms for solving sparse discrete optimization problems are considered. The numerical example is provided. The benchmarking is done in order to define real computational capabilities of block elimination algorithms combined with SYMPHONY solver. Analysis of the results show that for sufficiently large number of blocks and small enough size of separators between the blocks...
متن کاملFast thresholding algorithms with feedbacks for sparse signal recovery
We provide another framework of iterative algorithms based on thresholding, feedback and null space tuning for sparse signal recovery arising in sparse representations and compressed sensing. Several thresholding algorithms with various feedbacks are derived, which are seen as exceedingly effective and fast. Convergence results are also provided. The core algorithm is shown to converge in finit...
متن کاملA Framework for Adapting Population-Based and Heuristic Algorithms for Dynamic Optimization Problems
In this paper, a general framework was presented to boost heuristic optimization algorithms based on swarm intelligence from static to dynamic environments. Regarding the problems of dynamic optimization as opposed to static environments, evaluation function or constraints change in the time and hence place of optimization. The subject matter of the framework is based on the variability of the ...
متن کاملOptimal Algorithms for Distributed Optimization
In this paper, we study the optimal convergence rate for distributed convex optimization problems in networks. We model the communication restrictions imposed by the network as a set of affine constraints and provide optimal complexity bounds for four different setups, namely: the function $F(\xb) \triangleq \sum_{i=1}^{m}f_i(\xb)$ is strongly convex and smooth, either strongly convex or smooth...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SIAM Journal on Optimization
سال: 2020
ISSN: 1052-6234,1095-7189
DOI: 10.1137/18m1219187