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The lack of adequate measures is often an impediment to robust policy evaluation. We discuss three approaches to measurement and data usage that have the potential to improve the way we conduct impact evaluations. First, the creation of new measures, when no adequate ones are available. Second, the use of multiple measures when a single one is not appropriate. And third, the use of machine learning algorithms to evaluate and understand programme impacts. We motivate the relevance of each of the categories by providing examples where they have proved useful in the past. We discuss the challenges and risks involved in each strategy and conclude with an outline of promising directions for future work. [ABSTRACT FROM AUTHOR]

Derivative-free optimization focuses on designing methods to solve optimization problems without the analytical knowledge of the function. In this paper, we consider the problem of designing derivative-free methods for finite minimax problems: minxmaxi=1, 2, …, Nfi(x). In order to solve the problem efficiently, we seek to exploit the smooth substructure within the problem. Using ideas developed by Burkeet al.[J.V. Burke, A.S. Lewis, and M.L. Overton,Approximating subdifferentials by random sampling of gradients, Math. Oper. Res. 27(3) (2002), pp. 567–584; J.V. Burke, A.S. Lewis, and M.L. Overton,A robust gradient sampling algorithm for nonsmooth, nonconvex optimization, SIAM J. Optim. 15(3) (2005), pp. 751–779 (electronic)], we create the idea of a robust simplex gradient descent direction and use it to accelerate convergence. Convergence is proven by showing that the resulting algorithm fits into the directional direct-search framework. Numerical tests demonstrate the algorithm's effectiveness on finite minimax problems. [ABSTRACT FROM AUTHOR]