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Title
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A limited memory q-BFGS algorithm for unconstrained optimization problems
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Type
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JournalPaper
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Keywords
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Unconstrained optimization · Large-scale optimization · Quasi-Newton method · Limited memory BFGS method
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Abstract
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A limited memory q-BFGS (Broyden–Fletcher–Goldfarb–Shanno) method is presented for solving unconstrained optimization problems. It is derived from a modified BFGS-type update using q-derivative (quantum derivative). The use of Jackson’s derivative is an effective mechanism for escaping from local minima. The q-gradient method is complemented to generate the parameter q for computing the step length in such a way that the search process gradually shifts from global in the beginning to almost local search in the end. Further, the global convergence is established under Armijo-Wolfe conditions even if the objective function is not convex. The numerical experiments show that proposed method is potentially efficient.
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Researchers
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Bhagwat Ram (Not In First Six Researchers), Mohammad Esmael Samei (Fifth Researcher), Suvra Kanti Chakraborty (Fourth Researcher), Geetanjali Panda (Third Researcher), Shashi Kant Mishra (Second Researcher), Kin Keung Lai (First Researcher)
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