The goal of this paper is to study the problem of multivariate shape-constrained polynomial regression, which is the problem of fitting a multivariate polynomial regressor to datapoints with constraints on the shape of the regressor.
Apr 8, 2020 · We present a hierarchy of semidefinite programs (SDPs) for the problem of fitting a shape-constrained (multivariate) polynomial to noisy evaluations of an ...
Shape-Constrained Regression Using Sum of Squares Polynomials
pubsonline.informs.org › opre.2021.0383
Sep 6, 2023 · In this paper, we study a set of shape-constrained (multivariate) polynomial regressors, the sum of squares estimators (SOSEs), which are ...
Shape-Constrained Regression Using Sum of Squares Polynomials
pubsonline.informs.org › opre.2021.0383
Sep 6, 2023 · We present a hierarchy of semidefinite programs (SDPs) for the problem of fitting a shape-constrained (multivariate) polynomial to noisy ...
The authors present a hierarchy of semidefinite programs (SDPs) for the problem of fitting a shape-constrained (multivariate) polynomial to noisy ...
Apr 8, 2020 · We consider the problem of fitting a polynomial function to a set of data points, each data point consisting of a feature vector and a response ...
If we can find a way of imposing that a polynomial be nonnegative, then we are in business! • Unfortunately, hard to test whether a polynomial 𝑝 is ...
Apr 8, 2020 · We consider the problem of fitting a polynomial to a set of data points, each data point consisting of a feature vector and a response variable.
A hierarchy of semidefinite programs is proposed and it is established that polynomial functions that are optimal to any fixed level of this hierarchy form ...
Duration: 34:47
Posted: Jun 13, 2020
Posted: Jun 13, 2020