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Yes, a linear programming problem can have exactly two optimal solutions. This will be the case as long as only two decision variables are used within the problem.

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Q: Can a linear programming problem have exactly two optimal solutions?
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Can a linear programming problem have two optimal solutions?

No. However, a special subset of such problems: integer programming, can have two optimal solutions.


What is the similar property between dynamic programming and greedy approach?

Both are using Optimal substructure , that is if an optimal solution to the problem contains optimal solutions to the sub-problems


Advantages of dynamic programming?

Dynamic programming enables you to develop sub solutions of a large program.the sub solutions are easier to maintain use and debug.And they possess overlapping also that means we can reuse them.these sub solutions are optimal solutions for the problem


Can a linear programming problem have multiple optimal solutions?

When solving linear prog. problems, we base our solutions on assumptions.one of these assumptions is that there is only one optimal solution to the problem.so in short NO. BY HADI It is possible to have more than one optimal solution point in a linear programming model. This may occur when the objective function has the same slope as one its binding constraints.


What are the two properties that characterise a good dynamic programming problem?

Dynamic programming is a technique for solving problem and come up an algorithm. Dynamic programming divide the problem into subparts and then solve the subparts and use the solutions of the subparts to come to a solution.The main difference b/w dynamic programming and divide and conquer design technique is that the partial solutions are stored in dynamic programming but are not stored and used in divide and conquer technique.


Advantages and disadvantages Dynamic programming?

...........................Advantages and Disadvantages of Dynamc Programming..................Dynamic programming provide a polynomial time solution.also used in that problem in which repetition may occur.Recall that the Dynamic Programming method is applicable when an optimal solution can be obtained from a sequence of decisions......................................................................................By Adnantufail islamia college peshawar.


How do you solve a fixed cost problem?

Fixed Cost Problem is a kind of the Mixed Linear Programming Problem(MILP).Also, MILP is a Parametric Quadratic Concave Programming Problem. The optimal solution is existence of vertix set of the domain set. Then, you can use the domain cutting method.


What is optimal solution?

It is usually the answer in linear programming. The objective of linear programming is to find the optimum solution (maximum or minimum) of an objective function under a number of linear constraints. The constraints should generate a feasible region: a region in which all the constraints are satisfied. The optimal feasible solution is a solution that lies in this region and also optimises the obective function.


Can a linear programming problem have multiple solutions?

Yes. If the feasible region has a [constraint] line that is parallel to the objective function.


What is the relation between problem and programming?

Problem -> Programming Programming can be a solution to a problem. If you have a problem and it can be solved by a computer program, so you can create such a program - so you can solve this problem by programming.


What do you understand by linear programming problem?

1. What do you understand by Linear Programming Problem? What are the requirements of Linear Programming Problem? What are the basic assumptions of Linear Programming Problem?


What are the characteristics of integer programming problems?

The algorithms to solve an integer programming problem are either through heuristics (such as with ant colony optimization problems), branch and bound methods, or total unimodularity, which is often used in relaxing the integer bounds of the problem (however, this is usually not optimal or even feasible).