We consider the problem of estimating the true values of a Wiener process given noisy observations corrupted by outliers. The problem considered is closely related to the Trimmed Least Squares estimation problem, a robust estimation procedure well-studied from a statistical standpoint but poorly understood from an optimization perspective. In this paper we show how to improve existing mixed-integer quadratic optimization formulations for this problem. Specifically, we convexify the existing formulations via lifting, deriving new mixed-integer conic quadratic reformulations. The proposed reformulations are stronger and substantially faster when used with current mixed-integer optimization solvers. In our experiments, solution times are improved by at least two orders-of-magnitude.
Research report AG 19.05, ISE, University of Southern California, November 2019
View Outlier detection in time series via mixed-integer conic quadratic optimization