Random linear systems with sparse solutions -- finite dimensions
Mihailo Stojnic

TL;DR
This paper provides an exact analysis of the behavior of the $ ext{l}_1$ heuristic for sparse solutions in finite-dimensional random linear systems, complementing previous asymptotic results with novel geometric methods.
Contribution
It introduces new high-dimensional geometric strategies to precisely characterize the $ ext{l}_1$ heuristic's performance in non-asymptotic regimes, extending prior asymptotic analyses.
Findings
Exact non-asymptotic behavior of $ ext{l}_1$ in sparse recovery
Novel geometric methods for high-dimensional analysis
Complete characterization of $ ext{l}_1$ performance in finite dimensions
Abstract
In our companion work \cite{Stojnicl1RegPosasymldp} we revisited random under-determined linear systems with sparse solutions. The main emphasis was on the performance analysis of the heuristic in the so-called asymptotic regime, i.e. in the regime where the systems' dimensions are large. Through an earlier sequence of work \cite{DonohoPol,DonohoUnsigned,StojnicCSetam09,StojnicUpper10}, it is now well known that in such a regime the exhibits the so-called \emph{phase transition} (PT) phenomenon. \cite{Stojnicl1RegPosasymldp} then went much further and established the so-called \emph{large deviations principle} (LDP) type of behavior that characterizes not only the breaking points of the 's success but also the behavior in the entire so-called \emph{transition zone} around these points. Both of these concepts, the PTs and the LDPs, are in fact defined so that…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsProbabilistic and Robust Engineering Design · Markov Chains and Monte Carlo Methods
