Algorithms for Sparsity-Constrained Optimization

This thesis presents a wholly new technique in the structural analysis of data that uses a `greedy' algorithm to derive optimal sparse solutions, enabling faster and more accurate results in formerly problematic areas of machine learning and signal processing.