TY - JOUR
T1 - Constrained Gradient Descent and Line Search for Solving Optimization Problem with Elliptic Constraints
AU - Hasan, Ali A.
AU - Hasan, Mohammed A.
PY - 2003
Y1 - 2003
N2 - Finding global minima and maxima of constrained optimization problems is an important task in engineering applications and scientific computation. In this paper, the necessary conditions of optimality will be solved sequentially using a combination of gradient descent and exact or approximate line search. The optimality conditions are enforced at each step while optimizing along the direction of the gradient of the Lagrangian of the problem. Among many applications, this paper proposes learning algorithms which extract adaptively reduced rank canonical variates and correlations, reduced rank Wiener filter, and principal and minor components within similar framework.
AB - Finding global minima and maxima of constrained optimization problems is an important task in engineering applications and scientific computation. In this paper, the necessary conditions of optimality will be solved sequentially using a combination of gradient descent and exact or approximate line search. The optimality conditions are enforced at each step while optimizing along the direction of the gradient of the Lagrangian of the problem. Among many applications, this paper proposes learning algorithms which extract adaptively reduced rank canonical variates and correlations, reduced rank Wiener filter, and principal and minor components within similar framework.
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M3 - Conference article
AN - SCOPUS:0141788493
VL - 2
SP - 793
EP - 796
JO - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
JF - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
SN - 0736-7791
T2 - 2003 IEEE International Conference on Accoustics, Speech, and Signal Processing
Y2 - 6 April 2003 through 10 April 2003
ER -