https://github.com/yixuan/admm-spark
Implementation of ADMM algorithm on Apache Spark
https://github.com/yixuan/admm-spark
Last synced: 21 days ago
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Implementation of ADMM algorithm on Apache Spark
- Host: GitHub
- URL: https://github.com/yixuan/admm-spark
- Owner: yixuan
- Created: 2015-11-20T23:32:15.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2015-12-02T00:50:21.000Z (over 9 years ago)
- Last Synced: 2025-04-04T22:46:55.323Z (26 days ago)
- Language: Scala
- Size: 55.7 KB
- Stars: 25
- Watchers: 6
- Forks: 9
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ADMM-Spark
This repository is an experimental implementation of the
Alternating Direction Method of Multipliers (ADMM) algorithm on Apache Spark.
Currently the implemented model is the L1-regularized logistic regression.
## Dependencies
ADMM-Spark relies on the following libraries:
- Apache Spark (http://spark.apache.org/)
- Breeze (https://github.com/scalanlp/breeze, already included in Spark)
- Efficient Java Matrix Library (http://ejml.org/)
The sbt building tool will handle these dependencies. See the next section
for details.
## Installation
ADMM-Spark uses sbt (http://www.scala-sbt.org/index.html) to build the project
and manage the dependencies. To create the `jar` file, first enter the project
directory, and then type the `sbt` command.
```bash
cd ADMM-Spark
sbt
```
In sbt console, input `make` to build native C++ code that helps to speed up the
computation, and then enter `package` to compile the Scala source code and
create the `jar` file. If one also wants the `jar` file to include the
dependent libraries so that it can be directly used in Spark applications,
the `assembly` command can be used instead.
```bash
> make
Make dynamic library from C++ code
...
> package
[info] Packaging /target/scala-2.11/admm_2.11-1.0.jar ...
[info] Done packaging.
> assembly
...
[info] Done packaging.
```