Branch and Bound etc.

The three patterns in this set are Branch and Bound, Graphical Models and Structured Grids. First, we can talk about Branch and Bound. Amongst the four operations of branching, evaluation, bounding and pruning, I feel that the most important and valuable one would be the application of the objective function to a point in the search space. Evaluation becomes harder for larger search spaces. Pruning a particular branch using this strategy becomes pretty simple as it is quite easy to determine when the minimum value of a linear integer programming problem on a branch is greater than or equal to the current minimum possible solution. I feel that this kind of optimization is also very similar to the Monte Carlo pattern that we studied earlier in the class. The flowchart given in the description demonstrates this pattern very well. The second pattern is regarding Graphical models. Graphical Models is the next pattern in this set. This doesn't have much of a description though. In addition to the examples mentioned, I would like to add Bayesian inference as one of the possible algorithms using this pattern. Code snippets would have been helpful in understanding this pattern for people not familiar to the mechanisms described in the paper. The last pattern is about Structured Grids. We can observe that this is one among many patterns which are applicable to problems in image processing and in computer vision. It is mentioned in the context that the problems which follow this pattern perform updates until a particular error threshold is reached. It seems to me as this is closely linked to the granularity of the tasks to be performed. Overall, these set of patterns were more closely linked to problems that I could relate to in the real world , and were a very interesting read.