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Library for nonconvex constrained optimization using the augmented Lagrangian method and the matrix-free PANOC algorithm.

Home Page: https://tttapa.github.io/PANOC-ALM/Doxygen

License: GNU Lesser General Public License v3.0

CMake 7.10% CSS 1.39% HTML 0.43% Shell 3.66% Python 8.00% C++ 75.40% Jupyter Notebook 3.01% Dockerfile 0.16% TeX 0.34% Makefile 0.09% Batchfile 0.12% PowerShell 0.28% JavaScript 0.01%
optimization panoc augmented-lagrangian-method constrained-optimization

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panoc-alm's Issues

Make initial guess optional

In many solvers, a user-provided initial guess is optional, and I think it should be for panocpy as well. As a first solution, we could use all zeros as a default, which is what most users would provide anyway. We can think of a better initialization later.

Dimensionality check for initial guess

When a pa.Problem instance is called, no checks are currently performed to ensure that the dimension of the primal and dual initial guess are n and m respectively, which may silently lead to wrong results.

Make PANOCSolver configuration arguments optional

Instantiating a panocpy.PANOCSolver object without any arguments results in the error

TypeError: __init__(): incompatible constructor arguments. The following argument types are supported:
    1. panocpy._panocpy.PANOCSolver(arg0: panocpy._panocpy.PANOCParams, arg1: panocpy._panocpy.LBFGSDirection)
    2. panocpy._panocpy.PANOCSolver(arg0: panocpy._panocpy.PANOCParams, arg1: panocpy._panocpy.LBFGSParams)
    3. panocpy._panocpy.PANOCSolver(arg0: panocpy._panocpy.PANOCParams, arg1: panocpy._panocpy.PANOCDirection)

However, it would be convenient if default behavior was implemented, where arg0 simply is replaced with a new PANOCParams object and arg1 with a default instance of LBFGSParams.

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