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Combinatorial problems are found in several areas of computer science and in other disciplines wherein you apply computational methods including operations research, artificial intelligence, electronic commerce, and bioinformatics. Some of the best examples are finding out the cheapest and shortest round trips, deciding the 3D protein structure, etc. Other combinatorial problems you will face in scheduling, planning, and time-tabling. If you are looking for Combinatorial Problem Solving assignment help from BookMyEssay, you have reached the right place.

What is Combinatorial Problem Solving?

A combinatorial problem solving is selecting the best possible solution from a given set of probable solutions. One way of solving these problems is by using a cutting plane approach. In this method, a series of linear programming of an original problem is examined along with every problem in linear programming is obtained from an earlier relaxation by the addition of either one or more than one inequalities. If you face difficulty in writing assignments, you can always contact our experts for and ask, "write my homework for me on Combinatorial Problem Solving topic." Combinatorial problems occur in several areas of artificial intelligence, computer science, and operations research.


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  • Allocating register memory
  • Internet data pack routing
  • scheduling, planning, timetabling
  • protein structure prediction
  • portfolio selection
  • auctions winner determination

Solving Combinatorial Problems

The combinatorial problems are solved using the methods that are discussed in our Combinatorial Problem Solving assignment help in AUS as follows:

Explicit enumeration

Creating admissible solutions that are possible to receive the optimal solution. We have experts who have many years of experience and they can offer the best assistance.

Implicit enumeration

The admissible solutions that are considered and evaluated implicitly, however, are not built explicitly. The examples include Tree search, lower as well as upper limits regarding the value of an optimal solution.

Modeling problems along with linear programming (decision variables use integer values) or binary (variables having two possible values ; 0 and 1), and the resolution with algorithms. You can use these formulations to get limits regarding the value of optimal value.

Relaxation

When we do not consider the constraints of the actual problem, P O, the problem can be changed to another problem, which can simply solve P R. Considering the problem is minimal, its optimal values are related in the following manner:

f P R ≤ f P O

Linear relaxation can change an integer problem with constant variables by omitting the constraint. This enables you to use a Simplex Algorithm rather than a time consuming and a more complicated tree search.

Branch and bound

This method is dependent on the enumeration of solutions that are integer optimal by partitioning the solution space successfully and cutting a search tree through limits calculated at the time of enumeration.

Formulation issues

As there are various ways to represent the same problem mathematically and obtaining the perfect solution to greater integer programming within a reasonable computing time shall depend on the way it is formulated.

As mentioned by our Combinatorial Problem Solving coursework experts, plenty of research has been done for reformulating combinatorial problems. Regarding this, it is advantageous to enhance the number of integers or the number of constraints. Whenever a good formulation is discussed, an easy problem has created that works well.

As the integrality restrictions related to decision variables destroy the restriction, this approximation is called linear-programming relaxation. Solving combinatorial problems may be a tough job. The problem arises that unlike linear programming, a combinatorial problem’s feasible region is not convex.


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