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Thursday, June 22 • 4:57pm - 5:09pm
Cows, Lakes, and a JuMP Extension for Multi-stage Stochastic Optimization

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Stochastic Dual Dynamic Programming (SDDP) is an optimization algorithm for solving large, multi-stage stochastic programming problems. It is well known in the electricity community, but has received little attention in other application areas. The algorithm is computationally demanding as it typically involves iteratively solving hundreds of thousands of linear programs. In the past, implementations have been coded in slow, but expressive mathematical optimization languages such as AMPL, or in fast, but low level languages such as C++. In this talk, we detail a JuMP extension we have developed to solve problems using SDDP. We also present benchmarks showing that our Julia implementation has similar run-times to a previous version developed in C++, while being more flexible and expressive. This speed and flexibility has allowed us to revisit assumptions made in previous work, as well as apply the SDDP algorithm to problems as diverse as agriculture, energy, and finance.


Oscar Dowson

University of Auckland
Oscar Dowson (@odow) is a P.h.D. Candidate in Engineering Science at the University of Auckland. He works on applying stochastic optimization to the New Zealand dairy industry.

Thursday June 22, 2017 4:57pm - 5:09pm PDT
West Pauley Pauley Ballroom, Berkeley, CA