Implementation and acceleration of optimal control for systems biology

被引:16
作者
Sharp, Jesse A. [1 ,2 ]
Burrage, Kevin [1 ,2 ,3 ]
Simpson, Matthew J. [1 ]
机构
[1] Queensland Univ Technol, Sch Math Sci, Brisbane, Qld, Australia
[2] Queensland Univ Technol, ARC Ctr Excellence Math & Stat Frontiers, Brisbane, Qld, Australia
[3] Univ Oxford, Dept Comp Sci, Oxford OX2 6GG, England
基金
澳大利亚研究理事会;
关键词
convergence acceleration; forward-backward sweep method; optimal control; Wegstein; Aitken-Steffensen; Anderson; MULTIOBJECTIVE OPTIMAL-CONTROL; STEFFENSEN ITERATION METHOD; CONVERGENCE PROMOTION; ANDERSON ACCELERATION; CHEMICAL-PROCESSES; CELL MODEL; OPTIMIZATION; CHEMOTHERAPY; MECHANISMS; SIMULATION;
D O I
10.1098/rsif.2021.0241
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Optimal control theory provides insight into complex resource allocation decisions. The forward-backward sweep method (FBSM) is an iterative technique commonly implemented to solve two-point boundary value problems arising from the application of Pontryagin's maximum principle (PMP) in optimal control. The FBSM is popular in systems biology as it scales well with system size and is straightforward to implement. In this review, we discuss the PMP approach to optimal control and the implementation of the FBSM. By conceptualizing the FBSM as a fixed point iteration process, we leverage and adapt existing acceleration techniques to improve its rate of convergence. We show that convergence improvement is attainable without prohibitively costly tuning of the acceleration techniques. Furthermore, we demonstrate that these methods can induce convergence where the underlying FBSM fails to converge. All code used in this work to implement the FBSM and acceleration techniques is available on GitHub at https://github.com/Jesse-Sharp/Sharp2021.
引用
收藏
页数:20
相关论文
共 105 条
[71]   CONVERGENCE PROMOTION IN SIMULATION OF CHEMICAL PROCESSES WITH RECYCLE - DOMINANT EIGENVALUE METHOD [J].
ORBACH, O ;
CROWE, CM .
CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 1971, 49 (04) :509-&
[72]   Sequential Activation of Metabolic Pathways: a Dynamic Optimization Approach [J].
Oyarzun, Diego A. ;
Ingalls, Brian P. ;
Middleton, Richard H. ;
Kalamatianos, Dimitrios .
BULLETIN OF MATHEMATICAL BIOLOGY, 2009, 71 (08) :1851-1872
[73]   Task-level regulation enhances global stability of the simplest dynamic walker [J].
Patil, Navendu S. ;
Dingwell, Jonathan B. ;
Cusumano, Joseph P. .
JOURNAL OF THE ROYAL SOCIETY INTERFACE, 2020, 17 (168)
[74]   A Survey of Recent Trends in Multiobjective Optimal Control-Surrogate Models, Feedback Control and Objective Reduction [J].
Peitz, Sebastian ;
Dellnitz, Michael .
MATHEMATICAL AND COMPUTATIONAL APPLICATIONS, 2018, 23 (02)
[75]  
Penrose R, 1955, Math. Proc. Camb. Phil. Soc., V51, P406, DOI [10.1017/S0305004100030401, DOI 10.1017/S0305004100030401]
[76]   HISTORICAL SURVEY OF COMPUTATIONAL METHODS IN OPTIMAL CONTROL [J].
POLAK, E .
SIAM REVIEW, 1973, 15 (02) :553-584
[77]  
Pontryagin L.S., 1962, Mathematical Theory of Optimal Processes
[78]  
Press W. H., 2007, Numerical Recipes: 3rd Edition: The Art of Scientific Computing, V3
[79]   Iterative residual-based vector methods to accelerate fixed point iterations [J].
Ramiere, Isabelle ;
Helfer, Thomas .
COMPUTERS & MATHEMATICS WITH APPLICATIONS, 2015, 70 (09) :2210-2226
[80]  
Rao AV, 2010, ADV ASTRONAUT SCI, V135, P497