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Machine learning algorithms typically rely on optimization subroutines andare well-known to provide very effective outcomes for many types of problems.Here, we flip the reliance and ask the reverse question: can machine learningalgorithms lead to more effective outcomes for optimization problems? Our goalis to train machine learning methods to automatically improve the performanceof optimization and signal processing algorithms. As a proof of concept, we useour approach to improve two popular data processing subroutines in datascience: stochastic gradient descent and greedy methods in compressed sensing.We provide experimental results that demonstrate the answer is ``yes'', machinelearning algorithms do lead to more effective outcomes for optimizationproblems, and show the future potential for this research direction.

Data-driven Algorithm Selection and Parameter Tuning: Two Case studies in Optimization and Signal Processing