REVIEW 3 cited by
On the strong concavity of the dual function of an optimization problem
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
We provide three new proofs of the strong concavity of the dual function of some convex optimization problems. For problems with nonlinear constraints, we show that the the assumption of strong convexity of the objective cannot be weakened to convexity and that the assumption that the gradients of all constraints at the optimal solution are linearly independent cannot be further weakened. Finally, we illustrate our results with several examples.
Forward citations
Cited by 3 Pith papers
-
Overlapping Schwarz Preconditioners for Pose-Graph SLAM in Robotics
One-level additive overlapping Schwarz keeps CG iterations bounded (≤16) on growing synthetic 2D pose-graph SLAM problems, unlike unpreconditioned CG which grows past 10k iterations.
-
Learning more with the same effort: how randomization improves the robustness of a robotic deep reinforcement learning agent
Randomizing camera position during simulated robot-arm training improves robustness to viewpoint changes by about 25 percent average accuracy over fixed-camera training, at the same training budget.
-
Robust 2D lidar-based SLAM in arboreal environments without IMU/GNSS
A modified-Hausdorff-distance 2D lidar scan matcher fused with an EKF achieves reasonably accurate SLAM in orchards without IMU or GNSS, but results against A-LOAM are mixed.
Discussion (0). Continue with ORCID to comment.