Convex Function
A function is convex if the line segment between any two points on the graph of the function lies above or on the graph.
Category: General AI/ML
Uniform Law of Large Numbers
A statistical principle stating that the average of a sequence of random variables converges to the expected value as the number of variables increases.
Subdifferential
A generalization of the derivative for convex functions that allows for the analysis of nonsmooth optimization problems.
High-Dimensional Optimization
High-dimensional optimization involves finding optimal solutions in spaces with a large number of dimensions, which poses unique computational challenges.
Adaptive Lower Bound
An adaptive lower bound is a dynamic threshold that adjusts to avoid vacuous acceptance regions during optimization.
Worst-m Memory Mechanism
A memory mechanism that restricts comparisons to a fixed-size subset of past evaluations to improve computational efficiency.
Lipschitz Continuity
A function is Lipschitz continuous if there exists a constant such that the absolute difference in function values is bounded by this constant times the distance between input points.
Every Call is Precious (ECP)
ECP is an optimization framework that ensures each accepted function evaluation is potentially informative to the optimization process.
No-Regret Guarantees
No-regret guarantees ensure that the optimization algorithm performs nearly as well as the best fixed decision in hindsight, minimizing regret over time.
ECPv2
ECPv2 is a scalable algorithm designed for the global optimization of Lipschitz-continuous functions with unknown Lipschitz constants.