This talk presents my recent work on algorithms and software implementations to solve nonlinear stochastic programming problems to local and global optimality. Our algorithms exploit emerging high-performance computing hardware (e.g., multi-core CPUs, GPUs, and computing clusters) to achieve computational scalability. We are currently using our capabilities to address engineering and scientific questions that arise in diverse application domains including control of wind turbines, power management in large networks, and parameter inference in microbial community models. The problems that we are addressing are of unprecedented complexity and defy the state-of-the-art. For example, the problem of designing a control system for wind turbines is a nonlinear programming problem (NLP) with 7.5 million variables that takes days to solve with existing solvers. We have solved this problem in less than 1.3 hours using our parallel solvers.
Speaker(s): Professor Yankai Cao,
Location: Room: 418 Bldg: McLeod University of British Columbia 2332 Main Mall Vancouver, British Columbia V6T1Z4
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