= Random-restart optimization
<Random-restart optimization> repeatedly runs a local <optimization> <algorithm> from independently sampled starting points and retains the best result. It exploits multiple <basins of attraction> without requiring the local solver itself to escape a local <extremum>.
If each restart has <probability> $p$ of entering a desired basin, independent restarts miss it with <probability> $(1-p)^N$ after $N$ attempts. This formula quantifies the benefit only for the specified starting distribution and basin; neither $p>0$ nor a finite run certifies a global <extremum>. Recording the starting distribution and stopping rule makes computational comparisons reproducible.
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