Technology Category
Search and Optimization: Learning to SearchConstraint Satisfaction and Optimization: Constraint Learning and AcquisitionComputer Vision: Learning & Optimization for CV
FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guarantees
🤖 AI Summary
Traditional optimization methods for real-time constrained problems incur high computational overhead, while learning-based approaches often fail to strictly satisfy constraints. Method: This paper proposes FSNet, a neural network that guarantees feasibility by embedding a differentiable feasibility optimization step as an implicit layer within its architecture, enabling end-to-end differentiable training. Contribution/Results: FSNet is the first learning-based optimizer to simultaneously ensure (1) 100% constraint satisfaction in all outputs, (2) theoretical feasibility guarantees, and (3) provable convergence. By integrating differentiable optimization modeling, constraint-violation-minimizing loss, and gradient-based backpropagation, FSNet achieves significantly faster inference than conventional solvers across diverse tasks—including smooth/nonsmooth and convex/nonconvex problems—while attaining comparable or superior solution quality.
Technology Category
Application Category
📝 Abstract
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-Integrated 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.
Problem
Research questions and friction points this paper is trying to address.
Efficiently solving constrained optimization problems for real-time applications
Ensuring strict constraint enforcement in machine learning-based optimization approaches
Providing feasible solutions with guaranteed convergence and faster speeds
Innovation
Methods, ideas, or system contributions that make the work stand out.
Neural network integrates feasibility-seeking step
Differentiable optimization minimizes constraint violations
Ensures feasibility and convergence guarantees
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