Essay On Dynamic Programming

I started simulation by testing all the possible outcomes from challenge 1 to 5 and used the best scenario from one simulation to the other step by step and drew the conclusion at the very end.

For this assignment I develop and either pseudo code or a flowchart for my following programming problem.

In this assignment, I will be explaining very briefly some few concepts in order for you or someone else to understand the steps, the graphics and the written methodologies that I will be engaging in order to tackle this Programming Assignment task. In addition, I will be demonstrating how I arrived as my Choice and Solution.

The last part of the simulation for the year 2xx8 shows the decisions selected were the result of the “Ideal President”. The simulation showed the decisions made successfully handled the conditions in the economy and put the economy on the right track.

In the beginning of the simulation, I decided to choose my stocks wisely instead of impulse buying the stocks of companies I liked. I chose my stocks wisely by looking at how the stock has been doing over time. The stocks I chose were two shares of AbbVie for 131.82, 4 shares of Panera bread for 993.04, one share of Walgreens for 82.88 and 10 shares of Con.Ed for 784.30

During this round of the simulation DTL strategically leverage the cost and benefits of various

The fivestep process consists of: 1. IDENTIFY the system 's constraint(s). 2. Decide how to EXPLOIT

3. Explain how to apply each part of the IPDE process if you observe a child’s ball roll on to the road approximately 50 feet in front of you. IDENTIFY PREDICT DECIDE EXECUTE

Once the exercise was over and as the individual points were displayed a lot of things became clearer. Having experienced the system and then debriefing later on, helped in filling a lot of gaps in information that seemed to exist during the simulation. With a better understanding of ‘the rules of the game’ and some of the strategies that did work, it is difficult to introspect now without a bias. As the saying goes – “Hindsight is wonderful. It's always very easy to second guess after the fact. “– Helen Reddy

A* solves problems by starting from the initial node in the priority queue expanding nodes depending on heuristics until one of the nodes is the goal or the queue is empty. Hill climbing: Hill climbing using priority queue the priority is the state heuristics. the hill climbing solves problems by starting from the initial state and move to a neighbor with better heuristics. Repeat until all the neighboring states are of lower heuristics Return the current state as the solution state.

Slide 18 Simulation

Part II: The implementation of the program that runs the game involves testing. One of the necessary tests is to see if the simulated spins are random. Create an n-ary relation, in

Simulation is a computer process that gives a probable NPV or IRR for a project. All factors that affect the project’s returns are input. The computer then randomly selects one observation from each category. All of the observations are combined and the NPV or IRR is calculated from those figures. Simulation gives a range of outcomes as well as the probability of the outcomes. It provides the total risk level of a project.

The objective function, decision variables and constraints are fed into solver to arrive at the optimal solution as shown in the below screenshot

Exact optimisation method is the optimisation method that can guarantee to find all optimal solutions. In principle, the optimality of generated solution can be proofed mathematically. Therefore, exact optimisation is also termed as mathematical optimisation. However, exact optimisation approach is impractical usually. The effort of solving an optimisation problem by exact optimisation grows polynomially with the problem size. For example, to solve a problem by brute force approach, the execution time increases exponentially respect to the dimensions of the problem.