We give a rigorous complexity analysis of the simulated annealing algorithm by Kalai and Vempala (Math Oper Res 31(2):253–266, 2006) using the type of temperature update suggested by Abernethy and Hazan (International Conference on Machine Learning, 2016). The algorithm only assumes a membership oracle of the feasible set, and we prove that it returns a solution in polynomial time which is near-optimal with high probability. Moreover, we propose a number of modifications to improve the practical performance of this method, and present some numerical results for test problems from copositive programming.
|Journal||Journal of Optimization Theory and Applications|
|Publication status||Published - Aug 2022|
- Convex optimization
- Hit-and-run sampling
- Semidefinite and copositive programming
- Simulated annealing