One-Dimensional Nonnegative Spline Smoothing via Convex Semi-Infinite Programming with a Cutting-Plane Method

TL;DR

This paper introduces a cutting-plane method for efficiently solving one-dimensional nonnegative spline smoothing problems formulated as convex semi-infinite programming, improving convergence and solution quality over traditional approaches.

Contribution

The authors propose a novel cutting-plane algorithm that directly handles infinite inequality constraints in nonnegative spline smoothing, enhancing computational efficiency and solution accuracy.

Findings

The proposed method guarantees convergence to the true CSIP solution.

Numerical experiments show improved performance over conventional QP and LRSQP methods.

The approach effectively enforces nonnegativity without relying on sufficient conditions.

Abstract

Spline functions are smooth piecewise polynomials widely used for interpolation and smoothing, and nonnegative spline smoothing is also studied for nonnegative data. Previous research used sufficient conditions for the nonnegativity of spline functions because necessary and sufficient conditions for the nonnegativity are infinitely many linear inequalities, which are difficult to handle in optimization algorithms. This conventional method quickly computes a nonnegative spline function via quadratic programming (QP), but the optimal solution may be slightly degraded by using the sufficient condition. In this paper, we express 1D nonnegative spline smoothing as a convex semi-infinite programming (CSIP) problem that directly deals with infinite inequality constraints. As optimization algorithms for general SIP problems, local-reduction-based sequential quadratic programming (LRSQP) methods are used, but their convergence performance deteriorates for certain problems due to multiple approximations during updates. To quickly solve the CSIP problem, we propose a cutting-plane (CP) method. In the proposed method, after giving an initial solution by the standard spline smoothing, we find the minimizer of each polynomial piece by using the closed-form solution for a low-degree polynomial or a numerical solution for a high-degree polynomial. If the minimum value is negative, then such minimizer is added into the constraint of the problem to guarantee the nonnegativity. This constrained problem is quickly solved via QP, and we find the minimizer of each polynomial piece again. We repeat these procedures until there are no negative minimum values. The proposed method guarantees convergence to the original CSIP solution, and its effectiveness is demonstrated in numerical experiments by comparison to the conventional methods, QP under the sufficient condition and CSIP using the MATLAB LRSQP algorithm.