REVIEW 2 cited by
Solution uniqueness of convex optimization problems via the radial cone
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
In this paper, we mainly study solution uniqueness of some convex optimization problems. Our characterizations of solution uniqueness are in terms of the radial cone. This approach allows us to know when a unique solution is a strong solution or even a tilt-stable one without checking second-order information. Consequently, we apply our theory to low-rank optimization problems. The radial cone is fully calculated in this case and numerical experiments show that our characterizations are sharp.
Forward citations
Cited by 2 Pith papers
-
Stable Recovery of Regularized Linear Inverse Problems
A signal is stably recoverable from noisy linear measurements if and only if the kernel of the measurement operator intersects the tangent cone of the conjugate-subdifferential image only at zero.
-
Subspace decomposition in regularized least-squares: solution properties, restricted coercivity and beyond
A subspace decomposition gives explicit conjugate-form formulas for the solution set of regularized least squares, yielding unified existence, compactness, and uniqueness conditions.
Discussion (0). Continue with ORCID to comment.