Program Information
An Integrated Alternating Direction Method of Multipliers for Treatment Planning Optimization
M Zarepisheh1*, Y Ye2 , L Xing3 , (1) Stanford Univ School of Medicine, Stanford, CA, (2) Stanford Univ Management Science & Engineering, Stanford, CA
Presentations
SU-F-BRB-11 (Sunday, July 12, 2015) 4:00 PM - 6:00 PM Room: Ballroom B
Purpose: To propose a new parallel-friendly optimization algorithm, based on alternating direction method of multipliers (ADMM), for solving optimization problems emerging in treatment planning and imaging
Methods: ADMM, emerging as a powerful tool for distributed optimization, is employed here for IMRT treatment planning. We modify the existing ADMM by integrating that with two optimization techniques known as Barzilai-Borwein gradient method and line search. We apply original and integrated ADMM, with various penalty parameter values, on three IMRT treatment planning cases and we compare their performance. Each algorithm terminates once it found a solution within the relative error of 1E-4 from the optimal solution. We also compare the performance of original and integrated ADMM against the commercial optimization software CPLEX.
Results: Our experiments on three different cancer sites (prostate, head and neck, and GYN) indicate that integrated ADMM is much faster than original ADMM (5.8 times faster on average). Moreover, while the performance of original ADMM heavily depends on the selected penalty parameter, integrated ADMM performs very robust against the penalty parameter. Compared to the commercial software CPLEX, integrated ADMM turns out to be around 4-7 times faster.
Conclusion: Integrated ADMM is a very efficient optimization algorithm to cope with the large-scale optimization problems. It is a parallel-friendly optimization algorithm and provides an idea platform for cloud-based treatment planning and imaging.
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