%0 Journal Article
%T An Algorithm for the Derivative-Free Unconstrained Optimization Based on a Moving Random Cone Data Set
%A Mariam Almahdi Mohammed Mu¡¯lla
%J Open Access Library Journal
%V 6
%N 9
%P 1-11
%@ 2333-9721
%D 2019
%I Open Access Library
%R 10.4236/oalib.1105652
%X <div style="text-align:justify;">
	<span style="line-height:1.5;">In this paper, we suggest and analyze some new derivative free iterative methods for solving nonlinear equation   using a trust-region method. We also, give several examples to illustrate the efficiency of these methods. Comparison with other similar method is also given. This tech-nique can be used to suggest a wide class of new iterative methods for solving optimization problem. For, solving linearly unconst</span><span style="line-height:1.5;">rained optimi-zation problems without derivatives, a derivative-free Funnel method for unconstrained non-linear optimization is proposed. The study presents new interpolation-based techniques. The main work of this paper depends on some matrix computation techniques. A linear system is solved to obtain the required quadratic model at each iteration. Interpolation points are based on polynomial which is then minimized in a trust-region.</span>
</div>
%K Optimization Problem
%K Convergence
%K Trust-Region Methods
%K Model-Based Optimization
%K Derivative-Free Optimization
%K Interpolation Examples
%U http://www.oalib.com/paper/5415043