  The paper proposes a new algorithm for solving global univariate optimization
problems. The algorithm does not require convexity of the target function. For
a broad variety of target functions after performing (if necessary) several
evolutionary leaps the algorithm naturally becomes the standard descent (or
ascent) procedure near the global extremum. Moreover, it leads us to an
efficient numerical method for calculating the global extrema of univariate
real analytic functions.

Optimization and Control