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SPORCO

SParse Optimization Research COde (SPORCO)

Dry Water Slippers For Sport Surf Barefoot Footwear Socks Yellow Women Fitness Driving Swim House Shoes Yoga Summer Quick Beach Aqua Shoes Mens This library provides functions for sparse coding and dictionary learning, together with miscellaneous support functions for signal and image processing with sparse representations. The sparse coding algorithms are based on the ADMM framework; while similar codes for some of these functions can be found elsewhere, those provided here include enhancements that are not present in other publicly available codes.

There are two versions of SPORCO, one implemented in Matlab, and the other in Python (the Matlab version is indicated by an 'M' in the version number). Note that the Python version is under far more active development, and supports a number of features that the Matlab version does not (except for online convolutional dictionary learning, which is currently only supported in the Matlab version).

Matlab Version

The Matlab version is available here: Black Women's Boot Steve Leather Ankle Star Madden CSwRXq6 (gzipped tar file, 636k). Please read the instructions in the ReadMe file in the distribution before using the library.

Python Version

The Python version is available on GitHub and on Orangetime Red Outdoor Lining Slip Sneakers Women's On xwx1597 Snow Ankle Booties Winter Boots Comfort Fur Warm Waterproof 4vSq4Rfxrw. The current release can be installed using the source package available from Orangetime Red Outdoor Lining Slip Sneakers Women's On xwx1597 Snow Ankle Booties Winter Boots Comfort Fur Warm Waterproof 4vSq4Rfxrw or by doing "pip install sporco" (assuming that pip is installed and the command is issued with root privileges). Documentation is available at Indoor Animal Pink Plush Bear Cute Fuzzy Slipper Shoes Teddy Adult Bear House Itsy Aerusi Winter Bedroom Kid nwYqTxvpR4.

Convolutional Sparse Representations

The most significant contribution of this library is the inclusion of implementations of recent efficient algorithms for convolutional sparse coding and dictionary learning. These algorithms are described in the papers

This library has been used in a variety of signal and image processing applications of convolutional sparse coding methods, including

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License

This library was developed at Los Alamos National Laboratory, and has been approved for public release under the approval number LA-CC-14-057. It is made available under the terms of the BSD 3-Clause License.

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