  Machine learning algorithms typically rely on optimization subroutines and
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 learning
algorithms lead to more effective outcomes for optimization problems? Our goal
is to train machine learning methods 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 subroutines in data
science: stochastic gradient descent and greedy methods in compressed sensing.
We provide experimental results that demonstrate the answer is ``yes'', machine
learning algorithms do lead to more effective outcomes for optimization
problems, and show the future potential for this research direction.
