Feasible solution for Linear Optimization

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SUMMARY

The discussion focuses on the justification of degeneracy in the phase II simplex algorithm when there is a tie for the minimum b-ratio. It establishes that if a nonbasic variable increases and causes multiple basic variables to drop to zero, the next basic feasible solution is degenerate. Additionally, it addresses the proof that if a feasible solution exists, then any linear combination with a direction vector satisfying Ad=0 remains feasible, leading to the conclusion that if the transpose of C * d is non-negative, the linear program is unbounded.

PREREQUISITES
  • Understanding of the phase II simplex algorithm
  • Knowledge of linear programming concepts, including feasible solutions and unboundedness
  • Familiarity with vector operations and inner products
  • Basic principles of linear algebra, particularly regarding null spaces
NEXT STEPS
  • Study the implications of degeneracy in the simplex algorithm
  • Learn about the geometric interpretation of linear programming solutions
  • Explore the concept of unbounded linear programs and their characteristics
  • Investigate the role of shadow prices and sensitivity analysis in linear optimization
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Mathematicians, operations researchers, and students studying linear programming and optimization techniques will benefit from this discussion.

hkus10
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1) How to justify if there is a tie for the minimum b-ratio at some iteration of the phase II simplex algorithm, then the next basic feasible solution is degenerate.

I have no idea how to justify it. Please give me some direction

2) Max. z = transpose of C * the vector x
s.t. Ax less or = b
& X bigger or = to 0

Suppose d belong in R^n satisfies Ad=0 and d bigger or equal to 0
a) Prove that if u belongs to R^n is a feasible solution of P, then so is u+td for all 0 less than or equal to t belongs to R.
b) Use part (a) to prove that if the transpose of C * d >= 0, the P is unbounded.

I think the big problem here is that I do not know how to approach part a.
please give some insight for how to at least begin to solve this problem?

Thank you!
I appreciate your time!
 
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hkus10 said:
1) How to justify if there is a tie for the minimum b-ratio at some iteration of the phase II simplex algorithm, then the next basic feasible solution is degenerate.

I have no idea how to justify it. Please give me some direction

2) Max. z = transpose of C * the vector x
s.t. Ax less or = b
& X bigger or = to 0

Suppose d belong in R^n satisfies Ad=0 and d bigger or equal to 0
a) Prove that if u belongs to R^n is a feasible solution of P, then so is u+td for all 0 less than or equal to t belongs to R.
b) Use part (a) to prove that if the transpose of C * d >= 0, the P is unbounded.

I think the big problem here is that I do not know how to approach part a.
please give some insight for how to at least begin to solve this problem?

Thank you!
I appreciate your time!

About your first question: if a nonbasic variable xj increases from 0 to t and drives TWO (or more) basic variables xr and xs to zero at the same time, then we can decide to let, say, xs leave the basis (to be replaced by xj). The other variable xr remains basic but will be zero (because xj = t will be the new value of xj and we know already that xj = t drives xr to zero).

By the way, you can just write x >= 0, rather than writing out "x bigger or = 0"; similarly for Ax <= b, etc.

If your undefined problem P means the LP you wrote, then supposing Ad=0, d>=0, what can you say about x = u + td, t >= 0? Does it satisfy all the constraints of P? What is the resulting value of z(x) = c.x? (Here c,x are vectors, "." = inner product.)

RGV
 

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