RA-SQP achieves optimal O(epsilon^-4) gradient and O(epsilon^-2) linear-system complexity for equality-constrained stochastic optimization, and handles general nonlinear constraints via robust subproblems.
S2MPJ and CUTEst optimization problems for Matlab, Python and Julia
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
A new decoder for the SIF test problems of the CUTEst collection is described, which produces problem files allowing the computation of values and derivatives of the objective function and constraints of most \cutest\ problems directly within ``native'' Matlab, Python or Julia, without any additional installation or interfacing with MEX files or Fortran programs. When used with Matlab, the new problem files optionally support reduced-precision computations.
citation-role summary
dataset 1
citation-polarity summary
fields
math.OC 1years
2025 1verdicts
CONDITIONAL 1roles
dataset 1polarities
use dataset 1representative citing papers
citing papers explorer
-
Retrospective Approximation Sequential Quadratic Programming for Stochastic Optimization with General Deterministic Nonlinear Constraints
RA-SQP achieves optimal O(epsilon^-4) gradient and O(epsilon^-2) linear-system complexity for equality-constrained stochastic optimization, and handles general nonlinear constraints via robust subproblems.