The findings show that students who received integration prompts outperformed those who did not receive any in knowledge acquisition and problem representation for solving an ill-structured problem.
In this paper, we present a new method based on the Non-Uniform Rational B-Spline (NURBS) representation for solving the inverse problems arising in three-dimensional (3D) droplet shape prediction.
We define a unique algorithm representation for solving problems such as matrix multiplication, transitive closure, finding critical path in a graph, finding all-pairs shortest paths in a graph, etc.
Few decades have witnessed a great interest in image regularization, especially the sparse representation regularization for solving inverse problems.
A graphic representation of RACS for solving GTSP is shown in Fig. 1.
Furthermore, a simulation-based genetic algorithm using Prüfer number representation is designed for solving the proposed fuzzy programming models as well as their crisp equivalents.
With the thresholding representations (22), the IFTA for solving the regularization problem ((QP_{a}^{lambda})) can be naturally defined as x^{k+1}=G_{a, lambdamu}bigl(B_{mu} bigl(x^{k}bigr bigr),quad k=0,1,2,ldots, (23) where (B_{mu}(x^{k})=x^{k}+mu F(x^{k})^{top} b-F(x^{k} b-Fk})).
Although sparse representation can be used for solving a system of linear equations that are overdetermined as seen in [4], it has also shown promise for underdetermined systems as demonstrated in robust face recognition studies such as [39].
This is an essential issue, because it determines whether a priori symbolic representations can be of use for solving Bongard problems.
This method recasts the frequency response operator as a two point boundary value problem and uses state-of-the-art automatic spectral collocation techniques for solving integral representations of the resulting boundary value problems with accuracy comparable to machine precision.
