An Efficient Two-Step Approach to Fair and Sparse Transactions Allocation
Yifan He, Jian Guo, Ruoshi Shi, Yanlong Zhao · 2024
This study considers a practically important financial transactions allocation problem originated from interbank market. To achieve fairness and sparsity at the same time, we formulate the problem as a non-linear sparse optimization problem. A novel two-step algorithm that (1) finds the most sparse but not necessarily fair solution, (2) then utilizes iterative local adjustments to cope with non-linear fairness constraint is proposed. An adaptive parameter selection method to improve efficiency, avoiding time-consuming parameter search is devised. We provide theoretical guarantees that the two-step algorithm along with the adaptive parameter selection can always find a feasible solution with as much sparsity as possible. The effectiveness and efficiency of the algorithm is demonstrated by conducting empirical analysis on a real dataset from financial industry.