A python implementation of the algorithm used to generate optimal piecewise linear approximations of convex functions proposed by Imamoto and Tang [1]. The algorithm uses an iterative search to find ...

This repository houses a set of functions dedicated to the study of how well neural networks can learn a piecewise function dataset of varying complexity. You will find code dedicated to generating ...

Discover a groundbreaking method for optimizing nonlinear functions through piecewise linearization. Explore our paper for the best approximation techniques and real-world examples.

In this paper, a new methodology for curve approximation is presented. The method is suitable for both self-intersected and non self-intersected curves, it combines elements from graph theory and from ...

We study representations of piecewise-smooth signals on graphs. We first define classes for smooth, piecewise-constant, and piecewise-smooth graph signals, followed by a series of multiresolution ...

For this aim, we try to obtain the best approximation of a nonlinear function as a piecewise linear function. Our method is based on an optimization problem. The optimal solution of this optimization ...

In this problem, the optimal path is one that maximizes the expected utility, with the utility function being piecewise-linear and concave. Such a utility function can be used to approximate nonlinear ...

We define two functions f and g on the unit interval [0, 1] to be strongly conjugate $\operatorname {iff}$ there is an order-preserving homeomorphism h of [0, 1] such that g = h -1 fh (a minor ...