Lagrange | minimalist Jekyll theme for running a personal blog | Theme library

by LeNPaul HTML Version: v4.0.0 License: MIT

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kandi X-RAY | Lagrange Summary

Lagrange is a HTML library typically used in User Interface, Theme, Jekyll applications. Lagrange has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Lagrange is a Jekyll theme that was built to be 100% compatible with GitHub Pages. If you are unfamiliar with GitHub Pages, you can check out their documentation for more information. Jonathan McGlone's guide on creating and hosting a personal site on GitHub is also a good resource.

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Quality

Security

License

Reuse

Support

Lagrange has a low active ecosystem.

It has 593 star(s) with 586 fork(s). There are 10 watchers for this library.

It had no major release in the last 12 months.

There are 10 open issues and 32 have been closed. On average issues are closed in 96 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Lagrange is v4.0.0

Quality

Lagrange has no bugs reported.

Security

Lagrange has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Lagrange is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

Lagrange releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

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Lagrange Key Features

No Key Features are available at this moment for Lagrange.

Lagrange Examples and Code Snippets

No Code Snippets are available at this moment for Lagrange.

Community Discussions

Trending Discussions on Lagrange

Remove points from a plot legend

how to display cities in one dropdown based on selected state in other dropdown using json data in angular ionic?

it's possible to obtain the lagrange multipliers from an optimal solution in Pyomo?

Perform an exception for a list of funtions

Pyomo Building a Lagrangian or Selecting left hand side of constraints

Polynomial fitting with equal number of data points and coefficients

How To Use Variable Found With Macro Within Macro

How to get multipliers after solving a quadratic program in ojAlgo

Plotting orbits in python using integrate.solve_ivp

How to use warm_start with Dual variables in cvxpy

QUESTION

Remove points from a plot legend

Asked 2021-May-05 at 11:17

I have this code that shows the Lagrange interpolation between set of points(x,y cordination). Using matplotlib:

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ANSWER

Answered 2021-May-05 at 11:17

Break markers into new lines

import numpy as np from scipy.interpolate import lagrange import matplotlib.pyplot as plt

Source https://stackoverflow.com/questions/67399811

QUESTION

how to display cities in one dropdown based on selected state in other dropdown using json data in angular ionic?

Asked 2021-Apr-27 at 16:44

following are my files for html, .ts and json . As json data was very extensive therefore i have just added a few states and their cities. my 1st dropdown is showing all states. Now I want to match my 1st dropdown's selected value of state with a key "state" in "cities" object in my json file so i can populate 2nd dropdown with cities relevant to that state. and I want to do this in function "getCitiesForSelectedState". please help me find solution for this.

//.ts file

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ANSWER

Answered 2021-Apr-27 at 16:44

You can do it with the $event parameter. Make sure to compare your values safely.

If your value is not in the right type or has spaces or unwanted chars, this c.state == val might not work.

You can use the trim function to compare your value safely: c.state.trim() == val.trim()

HTML

Source https://stackoverflow.com/questions/67284495

QUESTION

it's possible to obtain the lagrange multipliers from an optimal solution in Pyomo?

Asked 2021-Apr-08 at 19:59

I'd like to know how it's possible to obtain the lagrange multipliers from an optimal solution in a Concrete model solved with glpk?

Thanks!

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ANSWER

Answered 2021-Apr-08 at 19:59

This works for me in gurobi, try it out and tell me if it works with glpk.

You need to prompt getting the Lagrange multipliers / dual variable in pyomo by putting the following line somewhere before solving your model:

Source https://stackoverflow.com/questions/66827883

QUESTION

Perform an exception for a list of funtions

Asked 2021-Mar-30 at 19:42

hello people (I am new to python) Question: i use ipywidgets with buttons, i want to call a list of function, sometimes there's a problem in a function (syntax, division by zero,...), i try to put an exception to pass the error and lunch the next function, don't work :(

I'm running in jupyter environment using python 3.8.5.final.0 and pandas 1.1.3 division by 0 problem of syntax

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ANSWER

Answered 2021-Mar-30 at 19:42

You're calling your functions at the wrong time. In

Source https://stackoverflow.com/questions/66869042

QUESTION

Pyomo Building a Lagrangian or Selecting left hand side of constraints

Asked 2021-Jan-08 at 22:52

I have a pyomo model "m" with 4 variables and several constraints (both equality and inequality) in the form:

Min F(G1,G2,D1,D2) st h=0 g<=0

Then I need to build the lagrangian function, which is something like this:

Briefly, lambda and mu are the duals of the constraints. So I need the objective function + dual1cons1 + dual2cons2 and so on.

I have literally no idea how to do this. The closest I got is with this:

...

ANSWER

Answered 2021-Jan-08 at 22:52

Pyomo constraints have a body attribute, and uslack and lslack functions that give you a sum expression of the constraint left hand side, the upper slack, and lower slack respectively. The `body attribute is what you want to multiply by lambda and mu. Here is an example with a simple constraint

Source https://stackoverflow.com/questions/65611138

QUESTION

Polynomial fitting with equal number of data points and coefficients

Asked 2020-Nov-06 at 20:37

I am currently experimenting with polynomial fitting using jupyter. The function below returns the least-square polynomial of degree m given the data points in xs with corresponding ys.

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ANSWER

Answered 2020-Nov-06 at 17:31

Your code appears correct; you re-discovered the issues with trying to invert nearly-singular matrices with finite-precision arithmetic. The matrix A looks like this

Source https://stackoverflow.com/questions/64715483

QUESTION

How To Use Variable Found With Macro Within Macro

Asked 2020-Oct-05 at 20:52

I have a subroutine finding the address of a cell containing a specific string. I am capturing the address of this cell as a variable x and then I want to select that cell. What I have thus far is:

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ANSWER

Answered 2020-Oct-05 at 20:52

I think you're being tripped up by using a SUB to set a variable value. A Function is the proper method to your issue.

Source https://stackoverflow.com/questions/64176884

QUESTION

How to get multipliers after solving a quadratic program in ojAlgo

Asked 2020-Sep-23 at 11:14

I implement a Sequential quadratic programming (SQP) optimizer and use ojAlgo for the quadratic programming (QP) subproblem.

My question is: How do I get hold of the "Lagrange multipliers" for the QP solution?

In the attached example code that solve an QP result.getMultipliers() only return an empty Optional.

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ANSWER

Answered 2020-Sep-18 at 09:09

I believe that is an Optional because it was (sometimes) too messy to map the Lagrange multipliers from the solver to the constraints of the model.

If you're implementing an SQP solver may I suggest that you don't implement it in terms of ExpressionsBasedModel, but delegate to the convex solvers directly. Build something that implements org.ojalgo.optimisation.Optimisation.Solver and delegate to the various classes in the org.ojalgo.optimisation.convex package. Then you code more directly with the matrices, vectors and multipliers.

To make that solver usable by ExpressionsBasedModel you also implement an org.ojalgo.optimisation.Optimisation.Integration and register that by calling ExpressionsBasedModel.addPreferredSolver(myIntegeration) or ExpressionsBasedModel.addFallbackSolver(myIntegeration).

Implementing a solver and making it usable from the modelling tool are two separate things.

Source https://stackoverflow.com/questions/63951773

QUESTION

Plotting orbits in python using integrate.solve_ivp

Asked 2020-Aug-23 at 14:34

Im trying to plot the orbit of jupiter around the sun (as well as 2 astroid clusters in the Lagrange points) using integrate.solve_ivp, but when i plot a graph of x position and y, I'm getting a spiral, rather than a stable orbit. Can anyone help?

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ANSWER

Answered 2020-Aug-23 at 14:34

These are typical symptoms of a wrong numerical method or wrong parameters to the method.

Reading the documentation, you can use several methods. For the default "RK45" I got what you described. However, using

Source https://stackoverflow.com/questions/63548018

QUESTION

How to use warm_start with Dual variables in cvxpy

Asked 2020-Aug-20 at 15:32

How do I set starting guess for dual variable in cvxpy? for a normal single value problem the solution is

...

ANSWER

Answered 2020-Aug-20 at 15:32

CVXPY does not currently support starting guesses, neither for primal or dual variables.

Source https://stackoverflow.com/questions/63466839

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Lagrange

To start using Jekyll right away with GitHub Pages, fork the Lagrange repository on GitHub. From there, you can rename your repository to USERNAME.github.io, where USERNAME is your GitHub username, and edit the settings.yml file in the _data folder to your liking. Ensure that you have a branch named gh-pages. Your website should be ready immediately at 'http://USERNAME.github.io'. Note: if you are hosting several sites under the same GitHub username, then you will have to use Project Pages instead of User Pages - just change the repository name to something other than 'http://USERNAME.github.io'. Head over to the _posts directory to view all the posts that are currently on the website, and to see examples of what post files generally look like. You can simply just duplicate the template post and start adding your own content.
For a full local installation of Lagrange, download your own copy of Lagrange and unzip it into it's own directory. From there, open up your favorite command line tool, enter bundle install, and then enter jekyll serve. Your site should be up and running locally at http://localhost:4000.

Support

If you would like to make a feature request, or report a bug or typo in the documentation, then please submit a GitHub issue. If you would like to make a contribution, then feel free to submit a pull request - as a bonus, I will credit all contributors below! If this is your first pull request, it may be helpful to read up on the GitHub Flow first. Lagrange has been designed as a base for users to customize and fit to their own unique needs. Please keep this in mind when requesting features and/or submitting pull requests. Some examples of changes that I would love to see are things that would make the site easier to use, or better ways of doing things. Please avoid changes that do not benefit the majority of users.

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