ADMM | Examples/code for the alternating direction method | Machine Learning library
kandi X-RAY | ADMM Summary
kandi X-RAY | ADMM Summary
Examples/code for the alternating direction method of multipliers (ADMM)
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Generate lowrank approximation for low rank approximation
- Add a proximal operator to the list
- Perform ADMM decomposition
- Lasso algorithm
ADMM Key Features
ADMM Examples and Code Snippets
Community Discussions
Trending Discussions on ADMM
QUESTION
Graphical Least Absolute Shrinkage and Selection Operator, has been introduced by Jerome Friedman, Trevor Hastie and Robert Tibshirani ("Sparse inverse covariance estimation with the graphical lasso",2014). They suggest the block coordinate-descent algorithm for the problem solution (see. "The Graphical Lasso: New Insights and Alternatives" by Rahul Mazumder and Trevor Hastie). I wrote this easy MATLAB code using CVX, given X
(regressor's matrix of size m,n
):
ANSWER
Answered 2021-Apr-09 at 21:34Provided
- There is a unique solution (which should be true if your objective is regularized, which it appears to be by the last term.)
- There are no bugs in either implementation.
Both implementations should return the exact same solution as the problem is a convex semidefinite program. The difference you should observe is
- Runtime, one will likely run longer than the other, I would bet your implementation uses a general-purpose solver package (CVX) so should be slower.
- Memory usage, once again, I would expect the general purpose (unutuned) package should consume more memory.
- Numerical stability, in general this some implementations will be much more numerically stable. That is, if you use a weak regularization (very small lambda) you may find that some implementations fail to converge while others still work.
For small and toy problems this should not be a big deal (which is usually the case if you are an academic.) If you are a person trying to do something useful in the real world, runtime & memory usage tend to be extremely important, as they control what size problems you can tackle with your approach.
The only way to know the relative limitations to each approach is to implement and try both! At the very least, I would implement and run both approaches as a sanity check that both implementations are likely correct (the chance of both implementations being incorrect and reporting the same results across a range on inputs is very low.)
QUESTION
I'm new to using Dask but have experienced painfully slow performance when attempting to re-write native sklearn functions in Dask. I've simplified the use-case as much as possible in hope of getting some help.
Using standard sklearn/numpy/pandas etc I have the following:
...ANSWER
Answered 2018-Nov-18 at 17:35A quote from the documentation you may want to consider:
For large arguments that are used by multiple tasks, it may be more efficient to pre-scatter the data to every worker, rather than serializing it once for every task. This can be done using the scatter keyword argument, which takes an iterable of objects to send to each worker.
But in general, Dask has a lot of diagnostics available to you, especially the scheduler's dashboard, to help figure out what your workers are doing and how time is being spent - you would do well to investigate it. Other system-wide factors are also very important, as with any computation: how close are you coming to your memory capacity, for instance?
In general, though, Dask is not magic, and when data fits comfortable into memory anyway, there will certainly be cases where dask add significant overhead. Read the documentation carefully on the intended use for the method you are considering - is it supposed to speed things up, or merely allow you to process more data than would normally fit on your system?
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install ADMM
You can use ADMM 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
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page