Uniformly Optimal and Parameter-free First-order Methods for Convex and Function-constrained Optimization

Optimal oracle complexity for convex function-constrained optimization, without requiring prior knowledge of smoothness parameters. The methods combine accelerated first-order algorithms with level-set formulations, adapting to Hölder smoothness without requiring its parameters in advance.