non-linear-optimization | Steepest Descent , Newton , Quasi-Newton and Conjugate

by tamland Python Version: Current License: GPL-3.0

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kandi X-RAY | non-linear-optimization Summary

non-linear-optimization is a Python library. non-linear-optimization has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However non-linear-optimization build file is not available. You can download it from GitHub.

Implementation of Steepest Descent, Newton, Quasi-Newton and Conjugate Gradient for non-linear unconstrained optimization

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non-linear-optimization has a low active ecosystem.

It has 23 star(s) with 3 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 749 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of non-linear-optimization is current.

Quality

non-linear-optimization has no bugs reported.

Security

non-linear-optimization has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

non-linear-optimization is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

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non-linear-optimization releases are not available. You will need to build from source code and install.

non-linear-optimization has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed non-linear-optimization and discovered the below as its top functions. This is intended to give you an instant insight into non-linear-optimization implemented functionality, and help decide if they suit your requirements.

Perform a quasi - Newton - Newton algorithm
Linear interpolation
Calculate the step length of a function
Evaluate a function fd
Returns true if fd is less than alpha
Calculate the gradient of an objective function
Wrapper function for Newton s method
R Steepest descent
Simple Backtracking algorithm

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non-linear-optimization Key Features

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non-linear-optimization Examples and Code Snippets

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Community Discussions

Trending Discussions on non-linear-optimization

Supplying a vector of inequalities/constraints to mystic

QUESTION

Supplying a vector of inequalities/constraints to mystic

Asked 2018-Nov-14 at 04:24

I am trying to supply constraints to a a function minimisation that I have hitherto been performing successfully with an unconstrained algorithm available via scipy (scipy.optimize.fmin_l_bfgs_b()).

Reading up (see, e.g, Python constrained non-linear optimization), I discovered a minimisation packed called mystic that seems to be what I need. My situation is as follows. I have a function of 3N variables (representing xyz position coordinates of N nodes), and I want to supply a list of constraints such that z/x = const. for each node. This makes for a total of N constraints. How do I do define/supply these constraints most efficiently for mystic()? Can the same constraint object be used with scipy.optimize.slsqp() as well? Since my constraints are linear, this should be a viable option too.

I tried the following, but it crashed my computer:

...

ANSWER

Answered 2018-Nov-14 at 04:24

I'm the mystic author. I believe what you are looking to do is something like this:

Source https://stackoverflow.com/questions/53273909

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Vulnerabilities

No vulnerabilities reported

Install non-linear-optimization

You can download it from GitHub.
You can use non-linear-optimization like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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