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Our exact solutions confirm known results in literature and allows us to fully characterize a new regularizer with its corresponding expected convergence rates. In terms of curvature we can derive a new inequality that can be used to compute an optimal sequence of diminishing step sizes by solving a differential equation. We introduce a definitional framework and theory that defines and characterizes a core property, called curvature, of convex objective functions. Students will demonstrate knowledge of the history, literature and function of the theatre, including works from various periods. Objectives are often written more in terms of teaching intentions and typically indicate the subject content. %X We study Stochastic Gradient Descent (SGD) with diminishing step sizes for convex objective functions. Goals and Objectives are similar in that they describe the intended purposes and expected results of. %C Proceedings of Machine Learning Research %B Proceedings of the 36th International Conference on Machine Learning %T Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD