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The Hybrid BFGS‐CG Method in Solving Unconstrained Optimization Problems

Author

Listed:
  • Mohd Asrul Hery Ibrahim
  • Mustafa Mamat
  • Wah June Leong

Abstract

In solving large scale problems, the quasi‐Newton method is known as the most efficient method in solving unconstrained optimization problems. Hence, a new hybrid method, known as the BFGS‐CG method, has been created based on these properties, combining the search direction between conjugate gradient methods and quasi‐Newton methods. In comparison to standard BFGS methods and conjugate gradient methods, the BFGS‐CG method shows significant improvement in the total number of iterations and CPU time required to solve large scale unconstrained optimization problems. We also prove that the hybrid method is globally convergent.

Suggested Citation

  • Mohd Asrul Hery Ibrahim & Mustafa Mamat & Wah June Leong, 2014. "The Hybrid BFGS‐CG Method in Solving Unconstrained Optimization Problems," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:507102
    DOI: 10.1155/2014/507102
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    References listed on IDEAS

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    1. Ludwig, Alexander, 2007. "The Gauss-Seidel-quasi-Newton method: A hybrid algorithm for solving dynamic economic models," Journal of Economic Dynamics and Control, Elsevier, vol. 31(5), pages 1610-1632, May.
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