{ "id": "1804.07565", "version": "v1", "published": "2018-04-20T11:57:20.000Z", "updated": "2018-04-20T11:57:20.000Z", "title": "Moments and convex optimization for analysis and control of nonlinear partial differential equations", "authors": [ "Milan Korda", "Didier Henrion", "Jean-Bernard Lasserre" ], "categories": [ "math.OC", "math.AP" ], "abstract": "This work presents a convex-optimization-based framework for analysis and control of nonlinear partial differential equations. The approach uses a particular weak embedding of the nonlinear PDE, resulting in a linear equation in the space of Borel measures. This equation is then used as a constraint of an infinite-dimensional linear programming problem (LP). This LP is then approximated by a hierarchy of convex, finite-dimensional, semidefinite programming problems (SDPs). In the case of analysis of uncontrolled PDEs, the solutions to these SDPs provide bounds on a specified, possibly nonlinear, functional of the solutions to the PDE; in the case of PDE control, the solutions to these SDPs provide bounds on the optimal value of a given optimal control problem as well as suboptimal feedback controllers. The entire approach is based purely on convex optimization and does not rely on spatio-temporal gridding, even though the PDE addressed can be fully nonlinear. The approach is applicable to a very broad class nonlinear PDEs with polynomial data. Computational complexity is analyzed and several complexity reduction procedures are described. Numerical examples demonstrate the approach.", "revisions": [ { "version": "v1", "updated": "2018-04-20T11:57:20.000Z" } ], "analyses": { "keywords": [ "nonlinear partial differential equations", "convex optimization", "broad class nonlinear pdes", "complexity reduction procedures", "infinite-dimensional linear programming problem" ], "note": { "typesetting": "TeX", "pages": 0, "language": "en", "license": "arXiv", "status": "editable" } } }