Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: We consider a broad class of first-order optimization algorithms which are oblivious, in the sense that their step sizes are scheduled regardless of the function under consideration, except for limited sideinformation such as smoothness or strong convexity parameters. With the knowledge of these two parameters, we show that any such algorithm attains an iteration complexity lower bound of Ω( √ L/ǫ) for L-smooth convex functions, and Ω̃( √ L/μ ln(1/ǫ)) for L-smooth μ-strongly convex functions. These lower bounds are stronger than those in the traditional oracle model, as they hold independently of the dimension. To attain these, we abandon the oracle model in favor of a structure-based approach which builds upon a framework recently proposed in [1]. We further show that without knowing the strong convexity parameter, it is impossible to attain an iteration complexity better than Ω̃ ((L/μ) ln(1/ǫ)). This result is then used to formalize an observation regarding L-smooth convex functions, namely, that the iteration complexity of algorithms employing time-invariant step sizes must be at least Ω(L/ǫ).

On the Iteration Complexity of Oblivious First-Order Optimization Algorithms