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In this work, we consider learning sparse models in large scale setting, where the number of samples and the feature dimension can grow as large as millions or billions. Two immediate issues occur under such challenging scenarios: (i) com- putational cost; (ii) memory overhead.
MCPy is a python library for McCormick relaxations with sub-gradients. This is quite useful for prototyping and testing new convex relaxation and global optimization algorithms.
Non-linear topology identification using Deep Learning. Sparsity (lasso) is enforced in the sensor connections. The non-convex and non-differentiable function is solved using sub-gradient descent algorithm.