Background of the problem
1.Divide and Conquer :-
Merge sort runs worst, best and average case in Θ(nlgn).
Divide-and-conquer, breaks a problem into subproblems that are similar to the original problem, recursively solves the subproblems, and finally combines the solutions to the subproblems to solve the original problem. You should think of a divide-and-conquer algorithm as having three parts:
1. Divide the problem into a number of sub-problems that are smaller instances of the same problem.
2. Conquer the sub-problems by solving them recursively. If they are satisfying enough, solve the sub-problems as base cases.
3. Combine the solutions to the sub-problems into the solution for the original problem.
2.Dynamic Programming
TCO 8—Given a more complex problem, develop a complete solution that includes a comprehensive statement of the problem, complete program design, and program documentation.
to find solutions to the errors that were found so that a reoccurrence of the same error doesn’t
These three approaches have all their advantages and disadvantages and allow a great number of combinations. Our idea is to implement a three-step solution.
However, this ‘missing ingredient,’ becomes a series of larger problems that have more complex beginnings. Because of this, the resolutions prove much harder
- I think what can be done is to focus mainly on statistics and data of the problems
target and solve each individual case, rather than using a ‘blanket programme. This makes sense to me as it reminds me of the variance in
Briefly explain the problem you have chosen. How does it arise, and what issues does it present?
Define the problem is the most useful strategy for me. It is often hard to identify the root of the problem when making a decision. For example, goal setting can be difficult when you have more than one goal that you want to accomplish. I have so many goals that I want to accomplish, but the problem is trying to finish them all at once. Doing so I never complete any goals. It becomes too stressful and overwhelming. Once I define the problem I can start organizing the information from what is important and what is more realistic to complete first.
One of the bigger examples used in the article was Target. Target created algorithms and means of understanding what a single person might need to buy, or what they can tell them they need to buy. Target can tell by what a person has bought, what that person will buy in the future. So, if the fictional Target shopper used in the article goes out and buys things that a pregnant woman would need, Target can used the information to determine that she will need products that are similar in the future. By figuring out that she is pregnant Target can send coupons and ads to her so that she will come again, and once she comes again they can reward her by giving her a discount or something free like a Starbucks.
When finding the real problem, it is key to use the K.T. problem analysis technique. This is essentially asking yourself a series of questions pertaining to what you think the problem is. These questions can be things like:
Define the Problem: Describe the type of case and what problem(s) or issue(s) should be the focus for
Market segmentation is an approach used by a company to select their target market and provide data for a marketing plan. “Market segmentation consist of a two-step process; naming broad product markets and segmenting these broad products-markets in order to select target markets and develop suitable marketing mixes” (Perreault, Cannon, & McCarthy, 2014, p.97). There are 4 categories pertaining to market segmentation; behavioral, geographic, demographic, and behavioral.
solution to any problem, there are multiple that needs to be put together to make one
Layering is the process which is used and helped to reduce the overall problem, which is huge in size, to number of sub problems, which are manageable is size.
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.