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

Machine learningalgorithms typically rely on optimization subroutinesand are 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 goal is to train machine learningmethods to automatically improve the performance of optimization and signal processing algorithms. As a proof of concept, we use our approach to improve two popular data processing subroutinesin data science: stochastic gradient descentand greedy methods in compressed sensing. We provide experimental results that demonstrate the answer is ``yes'', machine learningalgorithms do lead to more effective outcomes for optimization problems, and show the future potential for this research direction.