A Generalized $(k,m)$ Heron Problem:Optimality Conditions and Algorithm

TL;DR

This paper introduces a generalized $(k,m)$-Heron problem extending classical geometric distance problems, providing theoretical optimality conditions and a convergent algorithm with practical numerical validation.

Contribution

It formulates a convex optimization framework for the generalized problem, establishes fundamental optimality conditions, and proposes a convergent Projected Subgradient Algorithm.

Findings

Algorithm converges under diminishing step-size rule.

Numerical experiments demonstrate stability and efficiency.

Framework generalizes multiple geometric distance problems.

Abstract

This paper presents a new extension of the classical Heron problem, termed the generalized $(k,m)$-Heron problem, which seeks an optimal configuration among $k$ feasible and $m$ target non-empty closed convex sets in $\mathbb{R}^n$. The problem is formulated as finding a point in each set that minimizes the pairwise distances from the points in the $k$-feasible sets to the points in the $m$-target sets. This formulation leads to a convex optimization framework that generalizes several well-known geometric distance problems. Using tools from convex analysis, we establish fundamental results on existence, uniqueness, and first-order optimality conditions through subdifferential calculus and normal cone theory. Building on these insights, a Projected Subgradient Algorithm (PSA) is proposed for numerical solution, and its convergence is rigorously proved under a diminishing step-size rule. Numerical experiments in $\mathbb{R}^2$ and $\mathbb{R}^3$ illustrate the algorithm's stability, geometric accuracy, and computational efficiency. Overall, this work provides a comprehensive analytical and algorithmic framework for multi-set geometric optimization with promising implications for location science, robotics, and computational geometry.