Alexander Shapiro

The 2021 INFORMS John von Neumann Theory Prize is awarded to Alexander Shapiro for his foundational contributions to theory and computational methods for stochastic programming, as well as seminal contributions to nonlinear analysis. The outstanding breadth and depth of Dr. Shapiro's research, combined with his contributions to the mathematical optimization community, make him an outstanding recipient of this prestigious prize. Dr. Shapiro has had a formative impact on stochastic programming, with many influential papers on the topic and two excellent and highly cited books on the subject (one joint with A. Ruszczynski "Stochastic Programming," Elsevier, 2003, and the other joint with D. Dentcheva and A. Ruszczynski "Lectures on Stochastic Programming," SIAM, 2009, 2014). Of particular note is his pioneering contribution to the complexity analysis of stochastic programming, which builds upon his development (since the 1980s) of a large and very influential body of work related to the asymptotic analysis and statistical inference of sample average approximations (SAA) of stochastic programs. His paper "Simulation-based optimization: Convergence analysis and statistical inference," published in Stochastic Models in 1996, investigates, for the first time in the then 40-year-old history of the subject, theoretical computational complexity of various generic stochastic programming problems. His recent work focuses on risk-averse decision making and includes development of a new modeling methodology for multistage risk-averse decision making, reducing the problem to a "nested" series of similar problems with smaller time horizons (and thus much more "computationally friendly" than the original multistage formulation of the problem). The techniques for multistage risk-averse decision making developed by Dr. Shapiro form the core methodology underlying Brazil's long-term planning of electric power generation. Dr. Shapiro is also well known for his contributions to sensitivity analysis and optimality conditions in continuous optimization, having developed important results for conic, nonsmooth and semi-infinite problems, problems involving matrix-valued functions and functions of eigenvalues of symmetric matrices, variational inequalities, and problems with equilibrium constraints.

Donald Goldfarb has made fundamental contributions to the field of continuous optimization through the design and analysis of innovative algorithms, including the celebrated BFGS quasi-Newton method for nonlinear optimization and the steepest edge simplex method for linear programming.

Alexander Shapiro has been one of the most prolific scholars in the field of Operations Research, contributing significantly to nonlinear analysis (specifically sensitivity and optimality), and to stochastic programming, where his work on complexity analysis and risk-averse decision making has been highly influential.

Selection Committee

Jorge Nocedal (Chair), Michael Todd, Jean-Philippe Vial, Laurence Wolsey.