Gradient-Only Online Convex Optimization with a Single Quadratic Leader
We study online convex optimization on a bounded Euclidean domain when the learner receives only one subgradient at its prediction and knows neither the horizon, a gradient bound, nor the losses'strong-convexity parameters. We present a scale-invariant algorithm that couples projected adaptive gradient descent to one c...