Solving Optimization Problem: Local Minima Traps & Solutions

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  • Thread starter Thread starter ggyyree
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SUMMARY

The discussion focuses on overcoming local minima traps in optimization problems using various methods. The user initially employed the Trust-Region Newton and Quasi-Newton methods but encountered local minima with different initial guesses. Suggestions provided include exploring the Random Walk method, Simulated Annealing, and implementing random restart variations on existing methods to escape local minima effectively.

PREREQUISITES
  • Understanding of Trust-Region Newton and Quasi-Newton optimization methods
  • Familiarity with local minima concepts in optimization
  • Knowledge of Simulated Annealing techniques
  • Basic principles of Random Walk methods
NEXT STEPS
  • Research advanced techniques for escaping local minima in optimization
  • Learn about the implementation of Simulated Annealing in practical scenarios
  • Explore random restart strategies for Trust-Region and Quasi-Newton methods
  • Investigate the effectiveness of Random Walk methods in optimization problems
USEFUL FOR

Mathematicians, data scientists, and optimization engineers seeking to enhance their problem-solving strategies in complex optimization scenarios.

ggyyree
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I met a problem about finding the optimization of some function. I used the Trust-Region Newton and Quasi-Newton methods for the problem; however, with different initial guesses I sometimes got the local minimums. May I ask how to get out the trap of the local minimums please?

I may try the Radom Walk method but it seems not be a good one. Any other ideas please reply! Thanks a lot!
 
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Simulated annealing? Random restart variations on your existing methods?
 

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