optimization-algorithms | Combinatorial optimization algorithms written in Python | Learning library

by allrod5 Python Version: Current License: GPL-3.0

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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, it has a Strong Copyleft License and it has low support. However optimization-algorithms build file is not available. You can download it from GitHub.

combinatorial optimization algorithms written in python 3.4 for solving timetabling problem scenarios. in this work algorithms for solving np-complete combinatorial problems of timetabling were made based on literature, which describes the relevant methods created until now for solving these kind of problems. the intention of this project was to compare the main algorithms described in the literature on solving timetabling problems. the scenarios provided for the algorithms are generated by another algorithm, also created in this work. by knowledge limitations of the author the structured programming paradigm was used to make all the algorithms, because of that the scenario generation algorithm demanded a lot of more time than expected to code it, this is also because of the complexity

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optimization-algorithms has a low active ecosystem.

It has 4 star(s) with 0 fork(s). There are 1 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 no bugs reported.

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

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optimization-algorithms 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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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.

Calculates the initial solution for each subject
Given a list of candidates and a list of candidates return the profiler
This function calculates the refactored subjects to refactored subjects
Convert subjects to Profiles
Search for tabu search
Get the neighborhood for each subject
Calculates cost between two states
Find the best best iteration
Perform a TabuSearch
Calculate the best iteration
Calculate the neighborhood of a subject
Calculates the cost between two moves
Assigns a probability distribution to each subject
Get the prof from the candidates
Prints class information
Creates a scenario
Example scenario
Schedules the profiler
Returns a help string
Creates a variables file
Generates a report for each subject

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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.

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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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