On the Performance-Complexity Tradeoff in Stochastic Greedy Weak Submodular Optimization

Weak submodular optimization underpins many problems in signal processing and machine learning. For such problems, under a cardinality constraint, a simple greedy algorithm is guaranteed to find a solution with a value no worse than 1 − e^−γ of the optimal. Given the high cost of queries to large-scale signal processing models, the complexity of GREEDY becomes prohibitive in modern applications. In this work, we study the tradeoff between performance and complexity when one resorts to random sampling strategies to reduce the query complexity of GREEDY. Specifically, we quantify the effect of uniform sampling strategies on the performance through two criteria: (i) the probability of identifying an optimal subset, and (ii) the suboptimality of the solution’s value with respect to the optimal. Building upon this insight, we propose a simple progressive stochastic greedy algorithm, study its approximation guarantees, and consider its applications to dimensionality reduction and feature selection tasks.