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Title: | Semidefinite Representations in Semialgebraic Optimization and Dynamics-Oriented Learning |

Authors: | El Khadir, Bachir |

Advisors: | Ahmadi, Amir Ali |

Contributors: | Operations Research and Financial Engineering Department |

Keywords: | Convex Optimization Dynamical Systems Learning Semidefinite programming Sum of squares |

Subjects: | Mathematics Operations research |

Issue Date: | 2020 |

Publisher: | Princeton, NJ : Princeton University |

Abstract: | Semidefinite programming (SDP) has known a recent surge in popularity in the last few decades in the mathematical optimization community. This surge could be explained by two main reasons. On the one hand, a mature theoretical and computational foundation backing SDP allows one to efficiently compute solutions and obtain formal guarantees on their quality. On the other hand, breakthrough developments connecting SDP to real algebraic geometry have resulted in fundamentally new approaches to tackling semialgebraic optimization problems. The same reasons also make SDP-based approaches well-suited for applications in dynamical systems theory where systems of interest can often be described by algebraic data, and where one seeks to certify various notions of stability, safety, and robustness on the behavior of these systems. The contribution of the first part of this thesis is at the interface of SDP and semialgebraic optimization. We start by presenting a framework for semidefinite programs whose objective function and constraints vary polynomially with time. Our goal is then to find a time-dependent solution that maximizes the aggregate of the objective function over time while satisfying the constraints at all times. We then turn our attention to studying the relationship between two notions that are known to make semialgebraic optimization problems more tractable: the geometric notion of convexity and the algebraic one of being a sum of squares. The interplay between the two notions is poorly understood. For instance, to this date, no example of a convex homogeneous polynomial (of any degree $d$ and in any number of variables $n$) that is not a sum of squares is known. We show that no such example exists in the case where $n=4$ and $d=4$, and if a certain conjecture of Blekherman related to the so-called \emph{Cayley-Bacharach} relationships is true, no such example exists in the case where $n=3$ and $d=6$ neither. These were the two minimal cases where one would have any hope of seeing convex homogeneous polynomials that are not sums of squares, due to known obstructions. %The main ingredient of the proof is a generalization of %the classical Cauchy-Schwarz inequality. In the second part of this thesis, we study the power of SDP and semialgebraic optimization when applied for the task of analyzing dynamical systems. First, we focus on the notion of stability --- one of the most fundamental properties that a dynamical system can verify. An ingenious idea, which was proposed by Lyapunov in his thesis at the end of the $19\text{th}$ century, turns the question of testing whether a dynamical system is locally asymptotically stable to the question of existence of an associated \emph{Lyapunov} function; but it was an open question for some time whether this Lyapunov function could be assumed to be a polynomial in the special case where the dynamical system is given by a polynomial vector field with rational coefficients. We give the first example of a globally (and in particular, locally) asymptotically stable polynomial vector field with integer coefficients that does not have an analytic Lyapunov function, let alone a polynomial one. We show by contrast that an asymptotically stable dynamical system given by a {homogeneous} vector field admits a {rational} (i.e., ratio of two polynomials) Lyapunov function, and we give a hierarchy of semidefinite programs that is guaranteed to find this Lyapunov function in finite time. Interestingly, the same tools that help analyze dynamical systems can also be applied for learning these dynamical systems from data. We develop a mathematical formalism of the problem of learning a vector field that fits sample measured trajectories and satisfies a concrete collection of local or global properties (side information). We extend results from constrained polynomial-approximation theory to show that, under some conditions, polynomial vector fields can fit the trajectories of any continuously-differentiable vector field to arbitrary accuracy while respecting side information. Furthermore, we show that SDP-based techniques are particularly suited for this learning task. We end by showing an application to imitation learning problems in robotics. |

URI: | http://arks.princeton.edu/ark:/88435/dsp01q524jr829 |

Alternate format: | The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog: catalog.princeton.edu |

Type of Material: | Academic dissertations (Ph.D.) |

Language: | en |

Appears in Collections: | Operations Research and Financial Engineering |

Files in This Item:

File | Description | Size | Format | |
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ElKhadir_princeton_0181D_13501.pdf | 11.14 MB | Adobe PDF | View/Download |

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