A smooth surface interpolation scheme for positive and convex data has been developed. This scheme has been extended from the rational quadratic spline function of Sarfraz [11] to a rational bi-quadratic spline function. Simple data dependent constraints are derived on the free parameters in the description of rational bi-quadratic spline function to preserve the shape of 3D positive and convex...

The interaction between linear, quadratic programming and regression analysis are explored by both statistical and operations research methods. Estimation and optimization problems are formulated in two different ways: on one hand linear and quadratic programming problems are formulated and solved by statistical methods, and on the other hand the solution of the linear regression model with con...

Mirror descent (MD) is a powerful first-order optimization technique that subsumes several algorithms including gradient (GD). In this work, we leverage quadratic constraints and Lyapunov functions to analyze the stability characterize convergence rate of MD algorithm as well its distributed variant using semidefinite programming (SDP). For both algorithms, consider strongly convex nonstrongly ...

In this paper, a kernel-free quadratic surface support vector regression with non-negative constraints (NQSSVR) is proposed for the problem. The task of NQSSVR to find function as function. By utilizing technique, model avoids difficulty choosing kernel and corresponding parameters, has interpretability certain extent. fact, data may have priori information that value response variable will inc...

In this work, we propose a tube-based model predictive control (MPC) scheme for state- and input-constrained linear systems subject to dynamic uncertainties characterized by integral quadratic constraints (IQCs). particular, extend the framework of $\rho$</tex-mat...