A neural network approximates the second-stage recourse model in two-stage stochastic Volt-VAR optimization, allowing the full problem to be solved as a mixed-integer linear program with over 50x speedup and sub-0.3% optimality gap on a 123-bus test system.
Deep neural networks and mixed integer linear optimization,
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A modified autoencoder with a custom embedding loss learns spatial mappings to solve the constrained unit disk problem for qubit embedding on neutral-atom quantum processors and outperforms classical solvers under fixed computation time.
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Neural Two-Stage Stochastic Volt-VAR Optimization for Three-Phase Unbalanced Distribution Systems with Network Reconfiguration
A neural network approximates the second-stage recourse model in two-stage stochastic Volt-VAR optimization, allowing the full problem to be solved as a mixed-integer linear program with over 50x speedup and sub-0.3% optimality gap on a 123-bus test system.
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Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks
A modified autoencoder with a custom embedding loss learns spatial mappings to solve the constrained unit disk problem for qubit embedding on neutral-atom quantum processors and outperforms classical solvers under fixed computation time.