Optimization-Algorithms | Optimization methods | Learning library

by Rachnog Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | Optimization-Algorithms Summary

Optimization-Algorithms is a Python library typically used in Tutorial, Learning, Example Codes applications. Optimization-Algorithms has no bugs, it has no vulnerabilities and it has low support. However Optimization-Algorithms build file is not available. You can download it from GitHub.

Optimization methods

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Optimization-Algorithms has a low active ecosystem.

It has 29 star(s) with 25 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

Optimization-Algorithms has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Optimization-Algorithms is current.

Quality

Optimization-Algorithms has 0 bugs and 0 code smells.

Security

Optimization-Algorithms has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Optimization-Algorithms code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Optimization-Algorithms does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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Optimization-Algorithms releases are not available. You will need to build from source code and install.

Optimization-Algorithms 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 Optimization-Algorithms and discovered the below as its top functions. This is intended to give you an instant insight into Optimization-Algorithms implemented functionality, and help decide if they suit your requirements.

Calculate the gradient of the Poisson distribution .
Run rozenbrock .
Calculate the conjugated direction of a point .
Calculate the Newton - Newton step .
Compute the svenn step .
Compute the partan function for a given point .
Implementation of Hook - Jeeves .
Calculate the Pauell stage .
Svenn stage method
Calculate the DSCowell Powell .

Get all kandi verified functions for this library.

Optimization-Algorithms Key Features

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Optimization-Algorithms Examples and Code Snippets

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Community Discussions

Trending Discussions on Optimization-Algorithms

Error in lme4::allFit() -- no applicable method for 'isGLMM'

Minimizing Function with vector valued input in MATLAB

QUESTION

Error in lme4::allFit() -- no applicable method for 'isGLMM'

Asked 2019-Dec-27 at 22:18

I'm hitting a confusing error while trying to run the lme4::allFit() using some built-in parallelization. I fit an initial model m0, which uses a larger dataframe ckDF (n = 265,623 rows) to model a binary response to a number of categorical and continuous predictors in a logistic framework with a random intercept for year.

I'm interested in determining whether different optimizers yield different results, following some recommendations I've found online (e.g. by @BenBolker here). My data is fairly large and takes ~20 minutes to run usually, so I'm hoping to use the parallel and ncpus parameters of allFit() to speed it up a bit. Here's my relevant code:

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ANSWER

Answered 2019-Dec-27 at 21:11

Thanks to @user20650 & @Ben Bolker for the tips in comments above -- it worked and I was able to get allFit() to run as expected, by ensuring I use parallel = "snow" in my function call since I'm running in Windows. Just posting the edited code here for anyone else who finds this useful:

Source https://stackoverflow.com/questions/59503685

QUESTION

Minimizing Function with vector valued input in MATLAB

Asked 2019-Apr-15 at 19:30

I want to minimize a function like below:

Here, n can be 5,10,50 etc. I want to use Matlab and want to use Gradient Descent and Quasi-Newton Method with BFGS update to solve this problem along with backtracking line search. I am a novice in Matlab. Can anyone help, please? I can find a solution for a similar problem in that link: https://www.mathworks.com/help/optim/ug/unconstrained-nonlinear-optimization-algorithms.html .

But, I really don't know how to create a vector-valued function in Matlab (in my case input x can be an n-dimensional vector).

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ANSWER

Answered 2019-Apr-15 at 19:30

You will have to make quite a leap to get where you want to be -- may I suggest to go through some basic tutorial first in order to digest basic MATLAB syntax and concepts? Another useful read is the very basic example to unconstrained optimization in the documentation. However, the answer to your question touches only basic syntax, so we can go through it quickly nevertheless.

The absolute minimum to invoke the unconstraint nonlinear optimization algorithms of the Optimization Toolbox is the formulation of an objective function. That function is supposed to return the function value f of your function at any given point x, and in your case it reads

Source https://stackoverflow.com/questions/55693436

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Vulnerabilities

No vulnerabilities reported

Install Optimization-Algorithms

You can download it from GitHub.
You can use Optimization-Algorithms 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.

Support

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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