A graph-conditioned meta-optimizer learns QAOA parameter trajectories from one problem class and transfers them to others, yielding better initializations than standard methods in an empirical study of 64 settings.
Learning to learn by gradient descent by gradient descent,
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Learned online policies for the ADMM relaxation parameter improve iteration count and runtime on benchmark quadratic programs while maintaining convergence guarantees for time-varying parameters under mild assumptions.
HUANet unrolls ADMM iterations into a trainable network that enforces equality constraints exactly via a differentiable correction layer and adds soft first-order optimality conditions during training.
citing papers explorer
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Graph-Conditioned Meta-Optimizer for QAOA Parameter Generation on Multiple Problem Classes
A graph-conditioned meta-optimizer learns QAOA parameter trajectories from one problem class and transfers them to others, yielding better initializations than standard methods in an empirical study of 64 settings.
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Learning Over-Relaxation Policies for ADMM with Convergence Guarantees
Learned online policies for the ADMM relaxation parameter improve iteration count and runtime on benchmark quadratic programs while maintaining convergence guarantees for time-varying parameters under mild assumptions.
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HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization
HUANet unrolls ADMM iterations into a trainable network that enforces equality constraints exactly via a differentiable correction layer and adds soft first-order optimality conditions during training.