Abstract
In this work we propose two greedy algorithms to solve the underdetermined nonlinear sparse recovery problem. There are hardly any algorithms to solve such problems. Our algorithm is based on the OMP (Orthogonal Matching Pursuit) and the CoSaMP (Compressive Sampling Matching Pursuit), two popular techniques for solving the linear problem. We empirically test the success rates of our algorithms on exponential and logarithmic functions. The success rates follow a pattern similar to the one we observe for linear recovery problems.
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Gupta, K., Majumdar, A. (2016). Greedy Algorithms for Non-linear Sparse Recovery. In: Singh, R., Vatsa, M., Majumdar, A., Kumar, A. (eds) Machine Intelligence and Signal Processing. Advances in Intelligent Systems and Computing, vol 390. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2625-3_9
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DOI: https://doi.org/10.1007/978-81-322-2625-3_9
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