S2MPJ and CUTEst optimization problems for Matlab, Python and Julia
Serge Gratton, Philippe L. Toint
TL;DR
This paper introduces a new decoder for the CUTEst test problems that enables direct computation of function values and derivatives within Matlab, Python, and Julia, simplifying the testing process and supporting reduced-precision calculations in Matlab.
Contribution
It presents a novel decoder for CUTEst problems that eliminates the need for external interfacing, streamlining optimization testing across multiple programming environments.
Findings
Supports direct computation of objective and constraint derivatives
Enables reduced-precision computations in Matlab
Simplifies integration of CUTEst problems in various programming languages
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.
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Taxonomy
TopicsComputational Physics and Python Applications · Parallel Computing and Optimization Techniques