Abstract
Simulated annealing is a powerful method to numerically treat nonlinear optimization problems. The algorithm is advantageous in respect to flexibility and independence of start conditions. However, a drawback is its slow rate of convergence compared to projection procedures.
© 1991 Optical Society of America
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