Degeneracy in Linear Programming

In summary, the conversation discusses the concept of degenerate basic solutions in a standard form polyhedron. In part (a), it is shown that if two different bases lead to the same basic solution, then the basic solution is degenerate. In part (b), it is questioned whether a degenerate basic solution can correspond to two or more distinct bases, and it is explained that this is not always true. In part (c), it is discussed that there may not always be an adjacent basic solution that is also degenerate.
  • #1
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Homework Statement



Consider the standard form polyhedron {x | Ax = b, x>=0} , and assume that the rows of the
matrix A are linearly independent.

(a) Suppose that two different bases lead to the same basic solution. Show that the basic solution is degenerate (has less than m non-zero entries).
(b) Consider a degenerate basic solution. Is it true that it corresponds to two or more distinct bases? Prove or give a counterexample.
(c) Suppose that a basic solution is degenerate. Is it true that there exists an adjacent basic solution which is degenerate? Prove or give a conterexample.

Homework Equations



The Attempt at a Solution



(a) I think it's obvious but how build the proof, the two different bases lead to the same basic solution, when the last entering variable cannot be increased at all because it's b value equals 0 therefore as result we have the same basic solution. But how to prove that?

(b) no, degenerate basic solution can correspond to one basis only as well. But how to prove that?
 
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  • #2
(c) no, it's not true that there exists an adjacent basic solution which is degenerate. But how to prove that?
 

Related to Degeneracy in Linear Programming

What is degeneracy in linear programming?

Degeneracy in linear programming refers to a situation where the optimal solution of a linear programming problem has multiple variables with a value of zero. This means that the solution is not unique and there are other solutions that can also be considered optimal.

What causes degeneracy in linear programming?

Degeneracy in linear programming can be caused by a variety of factors, including redundant constraints, extreme points that align with the boundary of the feasible region, and unbounded feasible regions.

How does degeneracy impact the solution of a linear programming problem?

Degeneracy can make the solution of a linear programming problem more difficult to find, as it can lead to cycling in the simplex algorithm and cause the algorithm to take longer to converge. It can also lead to multiple optimal solutions, which can complicate decision-making processes.

Can degeneracy be avoided in linear programming?

Degeneracy cannot always be avoided in linear programming, but it can be reduced by using a more efficient algorithm, such as the dual simplex method. Additionally, careful formulation of the problem and removing redundant constraints can also help reduce the likelihood of degeneracy.

What are some real-world applications of degeneracy in linear programming?

Degeneracy in linear programming can occur in a variety of real-world applications, such as resource allocation, transportation planning, and production scheduling. By understanding and managing degeneracy, these problems can be solved more efficiently and effectively.

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