PageRank can be computed either iteratively or algebraically. We can further improve upon this algorithm, by iteratively merging the two shortest arrays. It works by iteratively approximating the actual constrained optimization problem with linear programming problems. The algorithm works by iteratively selecting a node from the OPEN list and evaluating it. With this model, components are executed iteratively and in parallel, rather than linearly. The maximum-likelihood estimates are computed by using iteratively reweighted least squares. Bootstrapping is a technique used to iteratively improve a classifier's performance. Weights and biases are usually initialized randomly, and then updated iteratively during training through Backpropagation. Thus, the message can be decoded iteratively. There are also techniques for iteratively improving linear approximations (Matsui 1994). The idea is to integrate out lattice spins iteratively, generating a flow in couplings. They work by iteratively improving a complete assignment over the variables. Recent progress on this problem involves using an iteratively directional TV refinement for CS reconstruction. MPCA is solved by performing PCA in each mode of the tensor iteratively. This method is used in iteratively reweighted least squares.