The theoretical study of algorithms and data structures has been bolstered by worst-case analysis, where we prove bounds on the running time, space, approximation ratio, competitive ratio, or other measure that holds even in the worst case. Worst-case analysis has proven invaluable for understanding aspects of both the complexity and practicality of algorithms, providing useful features like the ability to use algorithms as building blocks and subroutines with a clear picture of the worst-case performance. More and more, however, the limitations of worst-case analysis become apparent and create new challenges. In practice, we often do not face worst-case scenarios, and the question arises of how we can tune our algorithms to work even better on the kinds of instances we are likely to see, while ideally keeping a rigorous formal framework similar to what we have developed through worst-case analysis.
A key issue is how we can define the subset of "instances we are likely to see." Here we look at a recent trend in research that draws on machine learning to answer this question. Machine learning is fundamentally about generalizing and predicting from small sets of examples, and so we model additional information about our algorithm's input as a "prediction" about our problem instance to guide and hopefully improve our algorithm. Of course, while ML performance has made tremendous strides in a short amount of time, ML predictions can be error-prone, with unexpected results, so we must take care in how much our algorithms trust their predictors. Also, while we suggest ML-based predictors, predictions really can come from anywhere, and simple predictors may not need sophisticated machine learning techniques. For example, just as yesterday's weather may be a good predictor of today's weather, if we are given a sequence of similar problems to solve, the solution from the last instance may be a good guide for the next.
No entries found