# A Monotonic Optimization Approach for Solving Strictly Quasiconvex Multiobjective Programming Problems

arXiv: https://arxiv.org/abs/2003.07549 · HTML: https://mlsanity.com/paper/2003.07549/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [Improving Pareto Front Learning via Multi-Sample Hypernetworks](https://mlsanity.com/paper/2212.01130/) (2022)
- [A Hyper-Transformer model for Controllable Pareto Front Learning with Split Feasibility Constraints](https://mlsanity.com/paper/2402.05955/) (2024)
- [Multiobjective Logistics Optimization for Automated ATM Cash Replenishment Process](https://mlsanity.com/paper/2304.13671/) (2023)
- [A Two-Phase Adaptive Balanced Penalty Method for Controllable Pareto Front Learning under Split Feasibility Conditions](https://mlsanity.com/paper/2605.19306/) (2026)

## Abstract (arXiv)

In this article, we use the monotonic optimization approach to propose an outcome-space outer approximation by copolyblocks for solving strictly quasiconvex multiobjective programming problems and especially in the case that the objective functions are nonlinear fractional. After the algorithm is terminated, with any given tolerance, we obtain an approximation of the weakly efficient solution set, that contains the whole weakly efficient solution set of the problem. The algorithm is proved to be convergent and it is suitable to be implemented in parallel using standard convex programming tools. Some computational experiments are reported to show the accuracy and efficiency of the proposed algorithm.