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 Post subject: Globalization and infeasibility strategies of SQP method
PostPosted: Fri Nov 17, 2023 11:30 am 
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Joined: Fri Nov 17, 2023 11:27 am
Posts: 2
Location: Berlin
Dear ALGLIB team and community,

I'm writing a paper on nonconvex optimization and I'm comparing several state-of-the-art solvers. I couldn't figure out how the ALGLIB SQP method is implemented, specifically:
- how it measures progress (filter or merit function). I think it then uses a line search to find an appropriate step length;
- how it deals with infeasible QPs. Does it relax the RHS or revert to minimizing constraint violation?
Hope you can help me out! Thanks a lot,

Charlie Vanaret

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Check out my next-generation solver for nonconvex optimization: https://github.com/cvanaret/Uno


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 Post subject: Re: Globalization and infeasibility strategies of SQP method
PostPosted: Sun Nov 19, 2023 7:17 pm 
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Joined: Fri May 07, 2010 7:06 am
Posts: 913
Hi!

ALGLIB uses an L1-penalized merit function to measure progress. It is a trust region method, i.e. it does not use a line search.

It uses SL1QP approach towards constraints and does not have separate feasibility enforcement stage. The merit function coefficient is dynamically chosen to ensure that there is a progress towards minimizing constraint violation.

Sergei.


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 Post subject: Re: Globalization and infeasibility strategies of SQP method
PostPosted: Sun Nov 19, 2023 8:09 pm 
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Joined: Fri Nov 17, 2023 11:27 am
Posts: 2
Location: Berlin
Hi Sergey,
Perfect, thanks for your answer. I'll post a link to the preprint once our article is submitted :)
Best,
Charlie

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Check out my next-generation solver for nonconvex optimization: https://github.com/cvanaret/Uno


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 Post subject: Re: Globalization and infeasibility strategies of SQP method
PostPosted: Mon Nov 20, 2023 4:43 pm 
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Actually, it is planned to switch to filters at some moment later. They seem to be more promising. But still the filter version will inherit many features of the original SL1QP one.


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