FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guarantees
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915573198225408 |
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| author | Nguyen, Hoang T. Donti, Priya L. |
| author_facet | Nguyen, Hoang T. Donti, Priya L. |
| contents | Efficiently solving constrained optimization problems is crucial for numerous real-world applications, yet traditional solvers are often computationally prohibitive for real-time use. Machine learning-based approaches have emerged as a promising alternative to provide approximate solutions at faster speeds, but they struggle to strictly enforce constraints, leading to infeasible solutions in practice. To address this, we propose the Feasibility-Seeking Neural Network (FSNet), which integrates a feasibility-seeking step directly into its solution procedure to ensure constraint satisfaction. This feasibility-seeking step solves an unconstrained optimization problem that minimizes constraint violations in a differentiable manner, enabling end-to-end training and providing guarantees on feasibility and convergence. Our experiments across a range of different optimization problems, including both smooth/nonsmooth and convex/nonconvex problems, demonstrate that FSNet can provide feasible solutions with solution quality comparable to (or in some cases better than) traditional solvers, at significantly faster speeds. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_00362 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guarantees Nguyen, Hoang T. Donti, Priya L. Machine Learning Optimization and Control Efficiently solving constrained optimization problems is crucial for numerous real-world applications, yet traditional solvers are often computationally prohibitive for real-time use. Machine learning-based approaches have emerged as a promising alternative to provide approximate solutions at faster speeds, but they struggle to strictly enforce constraints, leading to infeasible solutions in practice. To address this, we propose the Feasibility-Seeking Neural Network (FSNet), which integrates a feasibility-seeking step directly into its solution procedure to ensure constraint satisfaction. This feasibility-seeking step solves an unconstrained optimization problem that minimizes constraint violations in a differentiable manner, enabling end-to-end training and providing guarantees on feasibility and convergence. Our experiments across a range of different optimization problems, including both smooth/nonsmooth and convex/nonconvex problems, demonstrate that FSNet can provide feasible solutions with solution quality comparable to (or in some cases better than) traditional solvers, at significantly faster speeds. |
| title | FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guarantees |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2506.00362 |