A multiplicative Gauss-Newton minimization algorithm:Theory and application to exponential functions |
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Authors: | Anmol Gupta Sanjay Kumar |
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Institution: | Department of Electronics and Communication Engineering,Thapar Institute of Engineering and Tech-14ology,Patiala 147004,India |
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Abstract: | Multiplicative calculus(MUC) measures the rate of change of function in terms of ratios, which makes the exponential functions significantly linear in the framework of MUC.Therefore, a generally non-linear optimization problem containing exponential functions becomes a linear problem in MUC. Taking this as motivation, this paper lays mathematical foundation of well-known classical Gauss-Newton minimization(CGNM) algorithm in the framework of MUC. This paper formulates the mathematical derivation of proposed method named as multiplicative Gauss-Newton minimization(MGNM) method along with its convergence properties.The proposed method is generalized for n number of variables, and all its theoretical concepts are authenticated by simulation results. Two case studies have been conducted incorporating multiplicatively-linear and non-linear exponential functions. From simulation results, it has been observed that proposed MGNM method converges for 12972 points, out of 19600 points considered while optimizing multiplicatively-linear exponential function, whereas CGNM and multiplicative Newton minimization methods converge for only 2111 and 9922 points, respectively. Furthermore, for a given set of initial value, the proposed MGNM converges only after 2 iterations as compared to 5 iterations taken by other methods. A similar pattern is observed for multiplicatively-non-linear exponential function. Therefore, it can be said that proposed method converges faster and for large range of initial values as compared to conventional methods. |
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