https://github.com/ahmadyan/duplex

High performance optimization algorithm for nonconvex systems based on random tree search
https://github.com/ahmadyan/duplex

c-plus-plus machine-learning numerical-optimization optimization

Last synced: 8 months ago
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High performance optimization algorithm for nonconvex systems based on random tree search

• Host: GitHub
• URL: https://github.com/ahmadyan/duplex
• Owner: ahmadyan
• License: mit
• Created: 2015-04-23T20:38:29.000Z (over 10 years ago)
• Default Branch: master
• Last Pushed: 2017-05-20T23:11:47.000Z (over 8 years ago)
• Last Synced: 2024-01-30T00:55:16.211Z (almost 2 years ago)
• Topics: c-plus-plus, machine-learning, numerical-optimization, optimization
• Language: C++
• Homepage: http://www.adel.ac
• Size: 24.5 MB
• Stars: 6
• Watchers: 3
• Forks: 1
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Duplex Optimization

[![CircleCI](https://circleci.com/gh/ahmadyan/Duplex.svg?style=svg)](https://circleci.com/gh/ahmadyan/Duplex)

[Duplex's website](http://adel.ac/duplex)

Duplex is a high-performance global optimization algorithm for nonconvex, nonlinear, and functional optimization problems. The following core capabilities are included:

* Duplex can find global optimum of nonlinear non-convex functions.

* Duplex implements variety of gradient-descent based optimizations internally, such as Momentum, AdaDelta, Adamax. Duplex can also optimize when the gradient information are not available.

* Duplex supports unsupervised learning algoriths for clustering. 

* If the gradient information are not available (for circuit optimization), duplex uses reinforcement learning to predict the landscape of energy function.

* Duplex supports Synopsys HSPICE for solving nonlinear systems.

The latest release and a complete manual may be found at the Duplex home page: http://adel.ac/duplex

## Dependencies

Duplex requires the following dependencies to be installed:

* [Eigen 3.3.0](http://eigen.tuxfamily.org) linear algebra library. Install eigen using 

		brew install eigen

* [Boost 1.51](http://www.boost.org) Duplex requires both headers and compiled binaries. Install using brew install boost

* [Config4cpp](http://www.config4star.org/) configuration library. The binaries for the config4cpp for macOS are already shipped with Duplex (/submodules/config4cpp/lib).

* [Pangolin](https://github.com/stevenlovegrove/Pangolin) for plotting and managing display. The Pangolin will replace the gnuPlot. Duplex will install Pangolin as a submodule in /submodules/Pangolin.

* GnuPlot for drawing plots *(Optional)*. Install gnuplot using

	brew install gnuplot

## Build

Duplex uses [CMake](www.cmake.org) as a build system.

	git clone git://github.com/ahmadyan/Duplex

	cd Duplex

	mkdir build	

	cd build

	cmake ..

	make

Use cmake .. -GXcode to generate the xcode project file (or other generators).

Few examples are provided in /bin directory. Few MATLAB tests are available in the /test directory.

## Usage

* ./duplex --config example.cfg

* Currently the duplex binaries and the configurations has to be in the same directory, so please output the binary in the bin folder.

* Example configuration files are available in the /bin directory

* If using hspice as a numerical simulator, User should set an environment variable DUPLEX_SIM_ID to an integer value. If you don't want to set the environment variable, run Duplex from the python wrapper (test/duplex.py) in test directory. Python wrapper requires Python 3+.

## Misc

Duplex uses llvm coding style. Any PR will be formatted using clang-format.