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    What are the differences between derivative free and derivative based optimization.
    written 4.2 years ago by gravatar for Sayali Bagwe Sayali Bagwe • 6.7k modified 4.2 years ago by gravatar for Sagar Narkar Sagar Narkar • 1.8k

    Mumbai University > Computer Engineering > Sem 7 > Soft Computing

    Marks: 5 Marks

    Year: Dec 2015
    mumbai university soft computing • 8.9k  views
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    written 4.2 years ago by gravatar for Sagar Narkar Sagar Narkar • 1.8k modified 4.2 years ago by gravatar for Sayali Bagwe Sayali Bagwe • 6.7k
    Derivative Free Optimization Derivative Based Optimization
    Derivative Free Optimization cannot be derived Derivative Based Optimization can be derived
    It makes use of evolutionary concepts. It does not makes use of evolutionary concepts.
    It is slower than Derivative Based Optimization. It is faster than Derivative Free Optimization.
    It makes use of random number generator to find the search directions. It does not makes use of random number generator to find the search directions.
    No analysis is done sue to randomness. Analysis is performed at every step.
    There is no need of differentiable function. There is need of differentiable function.
    Some Natural wisdom is used that is based on evolution & thermo Dynamics. No Natural wisdom is used.
    Techique: Simulated Annealing. Techique: Descent Method & Newton's Method.
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    Thanks sir for tabular format

    written 4.2 years ago by gravatar for kunal.virkar1 kunal.virkar1 • 0
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    written 4.2 years ago by gravatar for Sayali Bagwe Sayali Bagwe • 6.7k

    Derivative free optimization

    • Derivative free optimization is repeated evaluation of objective function
    • The concept are based on natures wisdom, such as evolution and thermodynamics
    • The analytic opacity knowledge about them are based on empirical studies.

    • Stopping condition in derivative free optimization:

      Let k denote an iteration count and fk denote the best objective function obtained at count k. stopping condition depends on

      • Computation time
      • Optimization goal;
      • Minimal Improvement
      • Minimal relative improvement

    Derivative based optimization

    • Derivative based optimization deals with gradient-based optimization techniques, capable of determining search directions according to an objective function’s derivative information

    • It is used in optimizing non-linear neuro-fuzzy models,

      – Steepest descent

      – Conjugate gradient
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