SMALLbox

h1. FAQ

h2. Q1: What is SMALLbox?

*A1:* *SMALLbox* is an evaluation framework for processing signals using adaptive sparse structured representations. SMALLbox is built within FP7 EU FET project called "SMALL" that is exploring new provably good methods to obtain inherently data-driven sparse models, which are able to cope with large-scale and complicated data. The main focus of research in the area of *sparse representations* is in developing reliable algorithms with provable performance and bounded complexity. There exist many applications for which it was proven beneficial to sparsely represent the data in some transform domain (i.e. "dictionary"). Moreover, the success of sparse models heavily depends on the choice of a “dictionary” to reflect the natural structures of a class of data. *Dictionary learning for sparse representation* deals with inferring such a dictionary from training data and is a key to the extension of sparse models for new exotic types of data.

SMALLbox provides an easy way to evaluate these methods against state-of-the art alternatives in a variety of standard signal processing problems. This is achieved trough a unifying interface that enables a seamless connection between the three types of modules: problems, dictionary learning algorithms and sparse solvers. In addition, it provides interoperability between existing state-of-the-art toolboxes. 

As an open source MATLAB toolbox, the SMALLbox can be seen as not only as a evaluation and educational tool, but as a tool for reproducible research in the sparse representations research community.

h2. Q2: How to install SMALLbox?

h2. Q3: What are the Problem, solver, and DL structures in SMALLbox?

h2. Q4: What is included in SMALLbox?

h2. Q5: How do I contribute?

h2. Q6: I want to add my solver to SMALLbox. How?

h2. Q7: I want to add my dictionary learning algorithm to SMALLbox. How?

h2. Q8: I want to add a new sparse representation problem. How? 

h2. Q9: I want to add a new problem for dictionary learning. How?