Comparison of parametric sparse recovery methods for ISAR image formation

Numerical methods for sparse recovery

These lecture notes are an introduction to methods recently developed for performing numerical optimizations with linear model constraints and additional sparsity conditions to solutions, i.e. we expect solutions which can be represented as sparse vectors with respect to a prescribed basis. Such a type of problems has been recently greatly popularized by the development of the field of nonadapt...

Errata: Sampling Trajectories for Sparse Image Recovery

A key theorem regarding the Restricted Isometry Property for short sampling trajectories contained a normalization error which renders the statement of the theorem incorrect. This note describes the original claim and proof, the normalization issue and how it impacts the proof, and several avenues for future investigation. 1 Shortest-path sensing The papers • R. Willett. “Short and Smooth Sampl...

Gradient-Based Methods for Sparse Recovery

The convergence rate is analyzed for the sparse reconstruction by separable approximation (SpaRSA) algorithm for minimizing a sum f(x) + ψ(x), where f is smooth and ψ is convex, but possibly nonsmooth. It is shown that if f is convex, then the error in the objective function at iteration k is bounded by a/k for some a independent of k. Moreover, if the objective function is strongly convex, the...

on the comparison of keyword and semantic-context methods of learning new vocabulary meaning

the rationale behind the present study is that particular learning strategies produce more effective results when applied together. the present study tried to investigate the efficiency of the semantic-context strategy alone with a technique called, keyword method. to clarify the point, the current study seeked to find answer to the following question: are the keyword and semantic-context metho...

ISAR Signal Formation and Image Reconstruction as Complex Spatial Transforms