An optimal algorithm for Global Optimization and adaptive covering

The general class of zero-order Global Optimization problems is split into subclasses according to a proposed "Complexity measure" and the computational complexity of each subclass is rigorously estimated. Then, the laboriousness (computational demand) of general Branch-and-Bound (BnB) methods is estimated for each subclass. For conventional "Cubic" BnB based on splitting an n-dimensional cube into $$2^n$$2n sub-cubes, both upper and lower laboriousness estimates are obtained. The value of the Complexity measure for a problem subclass enters linearly into all complexity and laboriousness estimates for that subclass. A new BnB method based on the lattice $$A_n^*$$Anź is presented with upper laboriousness bound that is, though conservative, smaller by a factor of $$O((4/3)^n)$$O((4/3)n) than the lower bound of the conventional method. The optimality of the new method is discussed. All results are extended to the class of Adaptive Covering problems--that is, covering of a large n-dimensional set by balls of different size, where the size of each ball is defined by a locally computed criterion.