How can you improve algorithm efficiency with dynamic programming?

Being able to step away from the specific domain and frame a problem in another context can be a big help, especially if you already have a solution for the abstract version. Once you find a suitable analog, you can see if the same scenario is applicable to other problems in the original domain, identifying areas of potential reuse.

In an agile development environment, I guess the bottom-up approach would be a very efficient way to start working on large dynamic problems. Personally, I don’t really come across very many large problems where I have to use the bottom-up approach. In my experience you can solve most problems more efficiently through building your pseudo code, and thinking everything through before you start programming. And as far as efficiency goes, what you are really striving for in any situation is a complexity of n or something very close to n. If you can get the complexity down to one or something less than and then you’re doing a good job.

This is much like another question in another one of the articles where there is an issue of how well you can refer factor code, and component code, using the existing libraries. I just took a course on AI and deep learning and that is another area where you would use this bottom up approach with some problems, mathematical analysis and trying to define how well the problem is solved.

This is kind of a funny issue if you ever grew up or lived around where AT&T took over large section of suburban IL. There is so much business and corporate espionage and theft going on that you can’t can write anything down on a piece of paper or put it on an unencrypted USB drive, keeping in mind that there was no widespread encryption for a very long time. So you won’t find this practice and use very often up until I guess now. The going practice was to find the expert to be able to do this in their head which is no easy task, considering most programmers did not start out with a homework intensive Math education that heavily taught this type of programming, vector math, or edit distances, matrices and you can guess whatever else.

A similar, real-life example where I had to apply the algorithm was filling the capacity of an electronic device with the capacity of the attachable devices. The algorithm prioritized filling the capacity with the largest items first. The algorithm was greedy, so it was very important to keep track of the number of overall iterations and stop after it reached a threshold to prevent a long runtime. This was especially important because the implementation was part of a web application that had to remain fast, while many users were provoking the running of the algorithm.

This is another incredibly easy problem to solve, and it doesn’t matter whether you are trying to find the largest items smallest items most expensive or cheap, or whatever. This makes it essential and a huge benefit to find a friend or colleague, who is a math expert, quite simply. turning these problems into problems with the complexity of 1, 2, 3 and so on from n, O of n, O log of n, and it is actually a very enlightening career experience.

Example: Solving root cause analysis for complex semiconductor. A fourth example of a problem that can be solved by dynamic programming is dividing an integrated issue on chips by small subproblems and then, combine back the outcome to determine the final root cause. Instead of using high volume data to compile do a classification model of the root causes, the goal is to discompose the model in smaller scale predicted models and then, recombine the outcome to obtain a final root cause classification. The goal is to reduce the cost of the data collection. Every Silicon wafer can have in the other of 200-2000 chips ($ 100-300K) , depending on the market segment (server, PC or Mobile). We need to minimize the data collection in real wafers.

Dynamic programming can be applied to many real life optimization problems like weighted job scheduling, assembly line operations, finding optimal network paths ( bellman Ford algorithm), interval scheduling etc. Dynamic programming divides the problem into overlapping subproblems and takes the most efficient path at each step. However, because it considers all possible options before making a decision, it is often slower than traditional programming approaches like the greedy method.. If you need the best possible solution, and have no time constraints, you can adopt dynamic programming, if you want a solution fast and as optimal as possible given the time constraint, use the greedy approach if applicable.

All of this is intuitively obvious. Finding an optimum solution often involves repetitive and recursive processes, which need to be worked through in order to arrive at said solution.