Narges Bidabadi
Faculty of Mathematics, Yazd University, Yazd, Iran
[ 1 ] - The modified BFGS method with new secant relation for unconstrained optimization problems
Using Taylor's series we propose a modified secant relation to get a more accurate approximation of the second curvature of the objective function. Then, based on this modified secant relation we present a new BFGS method for solving unconstrained optimization problems. The proposed method make use of both gradient and function values while the usual secant relation uses only gradient values. U...
[ 2 ] - A Free Line Search Steepest Descent Method for Solving Unconstrained Optimization Problems
In this paper, we solve unconstrained optimization problem using a free line search steepest descent method. First, we propose a double parameter scaled quasi Newton formula for calculating an approximation of the Hessian matrix. The approximation obtained from this formula is a positive definite matrix that is satisfied in the standard secant relation. We also show that the largest eigen value...
[ 3 ] - Using an Efficient Penalty Method for Solving Linear Least Square Problem with Nonlinear Constraints
In this paper, we use a penalty method for solving the linear least squares problem with nonlinear constraints. In each iteration of penalty methods for solving the problem, the calculation of projected Hessian matrix is required. Given that the objective function is linear least squares, projected Hessian matrix of the penalty function consists of two parts that the exact amount of a part of i...
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