Generative operators solve many convex problems with minimal parameters.
problem Worst-case parameter bounds limit the practical use of neural operators.
method Developed generative equilibrium operators (GEOs) using realizable finite-dimensional layers.
result GEOs can uniformly approximate solutions to convex optimization problems with logarithmic growth in parameters.