ECHO: Eigenvalue-Guided Homotopy Optimization for QUBO with Application to Health Insurance Plan Design — Code and Experimental Framework

This repository contains the complete experimental framework used in the research study:"ECHO: Eigenvalue-Guided Homotopy Optimization for QUBO with Application to Health Insurance Plan Design".The project implements a structured QUBO optimization model for regulated health insurance product design and evaluates several optimization methods including a newly introduced ECHO method (Eigenvalue-Guided Homotopy Optimization).The framework includes:• Implementation of the ECHO optimization algorithm• Baseline solvers including Greedy heuristic and Simulated Annealing• Optional exact optimization using the Gurobi solver• Synthetic actuarial data generation using a frequency–severity model• QUBO construction with regulatory and affordability constraints• Benchmark QUBO families including MaxCut, portfolio selection, and spectral-dense random QUBO instances• Full experiment pipelines reproducing the computational study reported in the manuscriptThe repository enables reproduction of the experiments reported in the paper, including the 520 structured insurance QUBO instances and additional benchmark experiments used to evaluate algorithmic generalization.The code is publicly available at:https://github.com/firaskhabour/echo-qubo-optimization