  Power system optimization problems which include the nonlinear AC power flow
equations require powerful and robust numerical solution algorithms. Within
this sub-field of nonlinear optimization, interior point methods have come to
dominate the solver landscape. Over the last decade, however, a number of
efficient numerical optimizers have emerged from the field of Machine Learning
(ML). One algorithm in particular, Adam, has become the optimizer-of-choice for
a massive percentage of ML training problems (including, e.g., the training of
GPT-3), solving some of the largest unconstrained optimization problems ever
conceived of. Inspired by such progress, this paper designs a parallelized
Adam-based numerical solver to overcome one of the most challenging power
system optimization problems: security and reserve constrained AC Unit
Commitment. The resulting solver, termed QuasiGrad, recently competed in the
third ARPA-E Grid Optimization (GO3) competition. In the day-ahead market
clearing category (with systems ranging from 3 to 23,643 buses over 48 time
periods), QuasiGrad's aggregated market surplus scores were within 5% of the
winningest market surplus scores. The QuasiGrad solver is now released as an
open-source Julia package: QuasiGrad.jl. The internal gradient-based solver
(Adam) can easily be substituted for other ML-inspired solvers (e.g., AdaGrad,
AdaDelta, RMSProp, etc.). Test results from large experiments are provided.
