Analysis of Optimization Algorithms via Sum-of-Squares
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In this work, we introduce a new framework for unifying and systematizing the performance analysis of first-order black-box optimization algorithms for unconstrained convex minimization over finite-dimensional Euclidean spaces. The low-cost iteration complexity enjoyed by this class of algorithms renders them particularly relevant for applications in machine learning and large-scale data analysis. However, existing proofs of convergence of such optimization algorithms consist mostly of ad-hoc arguments and case-by-case analyses. On the other hand, our approach is based on sum-of-squares optimization and puts forward a promising framework for unifying the convergence analyses of optimization algorithms. Illustrating the usefulness of our approach, we recover several known convergence bounds for four widely-used first-order algorithms in a unified manner, and also derive one new convergence result for gradient descent with Armijo-terminated line search.
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