Warm-starting active-set solvers using graph neural networks
Quadratic programming (QP) solvers are widely used in real-time control and optimization, but their computational cost often limits applicability in time-critical settings. To resolve this, we propose a learning-to-optimize approach using graph neural networks (GNNs) to predict active constraints in the dual active-set solver DAQP. Our method exploits the structural properties of QPs by representing them as bipartite graphs and learns to approximate the optimal active set for effectively warm-starting the solver. Across varying problem sizes, the GNN consistently reduces the number of solver iterations compared to cold-starting, while performance is comparable to a multilayer perceptron baseline. In contrast to the baseline, our GNN-based approach trained on varying problem sizes generalizes to unseen dimensions, demonstrating flexibility and scalability. These results highlight the potential of structure-aware learning to accelerate optimization in real-time applications such as model predictive control.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Warm-Starting Iterative Gaussian Processes for Faster Sequential Inference
Efficient Gaussian process (GP) inference is critical for sequential decision-making tasks such as active learning, online prediction, and Bayesian optimization. Iterative approaches of approximating the GP posterior usi…
Gaussian ProcessesActive LearningImproving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes
Scaling hyperparameter optimisation to very large datasets remains an open problem in the Gaussian process community. This paper focuses on iterative methods, which use linear system solvers, like conjugate gradients, al…
Gaussian ProcessesLearning-Augmented Scalable Linear Assignment Problem Optimization via Neural Dual Warm-Starts
The Linear Assignment Problem is a fundamental combinatorial optimization task where classical exact solvers ensure optimality but suffer from an $\mathcal{O}(N^{3})$ bottleneck, while recent neural approximations strugg…
Zero-shot GeneralizationLinear Complementarity for Regularized Policy Evaluation and Improvement
Recent work in reinforcement learning has emphasized the power of L1 regularization to perform feature selection and prevent overfitting. We propose formulating the L1 regularized linear fixed point problem as a linear c…
feature selectionReinforcement LearningReinforcement Learning (RL)MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers
Mixed-integer programming (MIP) technology offers a generic way of formulating and solving combinatorial optimization problems. While generally reliable, state-of-the-art MIP solvers base many crucial decisions on hand-c…
Combinatorial OptimizationGraph Neural Network