Robust optimization (RO) has attracted great attention from the optimization community over the past decade. RO is dedicated to solving optimization problems subject to uncertainty: design constraints must be satisfied for all the values of the uncertain parameters within a given uncertainty set. Uncertainty sets may be modeled as deterministic sets (boxes, polyhedra, ellipsoids), in which case the RO problem may be reformulated via worst-case analysis, or as families of distributions. The challenge of RO is to reformulate or approximate robust constraints so that the uncertain optimization problem is transformed into a tractable deterministic optimization problem. Most reformulation methods assume linearity of the robust constraints or uncertainty sets of favorable shape, which represents only a fraction of real-world applications. This survey addresses nonlinear RO and includes problem formulations and applications, solution approaches, and available software with code samples.
Leyffer, Sven, et al. "A survey of nonlinear robust optimization." INFOR. Information Systems and Operational Research, vol. 58, no. 2, Mar. 2020. https://doi.org/10.1080/03155986.2020.1730676
Leyffer, Sven, Menickelly, Matt, Munson, Todd, Vanaret, Charlie, & Wild, Stefan M. (2020). A survey of nonlinear robust optimization. INFOR. Information Systems and Operational Research, 58(2). https://doi.org/10.1080/03155986.2020.1730676
Leyffer, Sven, Menickelly, Matt, Munson, Todd, et al., "A survey of nonlinear robust optimization," INFOR. Information Systems and Operational Research 58, no. 2 (2020), https://doi.org/10.1080/03155986.2020.1730676
@article{osti_1660707,
author = {Leyffer, Sven and Menickelly, Matt and Munson, Todd and Vanaret, Charlie and Wild, Stefan M.},
title = {A survey of nonlinear robust optimization},
annote = {Robust optimization (RO) has attracted great attention from the optimization community over the past decade. RO is dedicated to solving optimization problems subject to uncertainty: design constraints must be satisfied for all the values of the uncertain parameters within a given uncertainty set. Uncertainty sets may be modeled as deterministic sets (boxes, polyhedra, ellipsoids), in which case the RO problem may be reformulated via worst-case analysis, or as families of distributions. The challenge of RO is to reformulate or approximate robust constraints so that the uncertain optimization problem is transformed into a tractable deterministic optimization problem. Most reformulation methods assume linearity of the robust constraints or uncertainty sets of favorable shape, which represents only a fraction of real-world applications. This survey addresses nonlinear RO and includes problem formulations and applications, solution approaches, and available software with code samples.},
doi = {10.1080/03155986.2020.1730676},
url = {https://www.osti.gov/biblio/1660707},
journal = {INFOR. Information Systems and Operational Research},
issn = {ISSN 0315-5986},
number = {2},
volume = {58},
place = {United States},
publisher = {Taylor & Francis},
year = {2020},
month = {03}}
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1660707
Journal Information:
INFOR. Information Systems and Operational Research, Journal Name: INFOR. Information Systems and Operational Research Journal Issue: 2 Vol. 58; ISSN 0315-5986
2004 IEEE International Symposium on Computer Aided Control Systems Design, 2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508)https://doi.org/10.1109/CACSD.2004.1393890