REVIEW 4 cited by
PENLAB: A MATLAB solver for nonlinear semidefinite optimization
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
PENLAB is an open source software package for nonlinear optimization, linear and nonlinear semidefinite optimization and any combination of these. It is written entirely in MATLAB. PENLAB is a young brother of our code PENNON \cite{pennon} and of a new implementation from NAG \cite{naglib}: it can solve the same classes of problems and uses the same algorithm. Unlike PENNON, PENLAB is open source and allows the user not only to solve problems but to modify various parts of the algorithm. As such, PENLAB is particularly suitable for teaching and research purposes and for testing new algorithmic ideas. In this article, after a brief presentation of the underlying algorithm, we focus on practical use of the solver, both for general problem classes and for specific practical problems.
Forward citations
Cited by 4 Pith papers
-
Input-to-state Stable Approximate Nonlinear Model Predictive Control with Realtime Feasibility
A precomputed ISS-CLF/robust-CBF pair yields a real-time QP that approximates robust NMPC with proven ISS and constraint satisfaction for nonlinear systems.
-
Scalable Learning of High-Dimensional Demonstrations with Composition of Linear Parameter Varying Dynamical Systems
A divide-and-conquer method learns stable linear parameter varying dynamical systems in high-dimensional robot joint space by optimizing subsystems and compositing them with a joint Lyapunov function.
-
Synthesis of safety certificates for discrete-time uncertain systems via convex optimization
The paper derives SDPs that co-design quadratic control barrier functions and linear feedback controllers, certifying worst-case or probabilistic safety for discrete-time uncertain linear systems.
-
On the Practical Implementation of a Sequential Quadratic Programming Algorithm for Nonconvex Sum-of-squares Problems
A filter line search SQP algorithm reduces iterations and computation time for nonconvex SOS programs compared to prior methods.
Discussion (0). Continue with ORCID to comment.