Rigorous finite-time guarantees for optimization on continuous domains by simulated annealing

Simulated annealing is a popular method for approaching the solution of a global optimization problem. Existing results on its performance apply to discrete combinatorial optimization where the optimization variables can assume only a finite set of possible values. We introduce a new general formulation of simulated annealing which allows one to guarantee finite-time performance in the optimization of functions of continuous variables. The results hold universally for any optimization problem on a bounded domain and establish a connection between simulated annealing and up-to-date theory of convergence of Markov chain Monte Carlo methods on continuous domains. This work is inspired by the concept of finite-time learning with known accuracy and confidence developed in statistical learning theory.

Analysis of spallation based upon statistical microdamage mechanics

A relationship between the cumulative length of microcracks and the amplitude and duration of tensile impulse in spallation was established based on the application of statistical microdamage mechanics, which included a statistical formulation and dynamic laws of microdamage under loading. Since the degrees of spallation, called incipient, intermediate and complete spallation, can be characterized by the cumulative length of microcracks, a physical interpretation of an empirical criterion to spallation was presented.