Look ahead parallel pursuit

We endeavor to improve compressed sensing reconstruction performance of parallel pursuit algorithms. In an iteration, standard parallel pursuit algorithms use a support-set expansion by a fixed number of coefficients, leading to restricted performance. To achive a better performance, we develop a look ahead strategy that adaptively chooses the best number of coefficients. We develop a new algorithm which we call look ahead parallel pursuit, where a look ahead strategy is invoked on a minimal residual norm criterion. The new algorithm provides a trade-off between performance and complexity.