Feasible solution for Linear Optimization

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In the discussion on linear optimization, participants seek clarification on the implications of a tie for the minimum b-ratio in the phase II simplex algorithm, specifically regarding the resulting basic feasible solution being degenerate. It is noted that if a nonbasic variable increases and causes multiple basic variables to drop to zero, this indicates a degenerate solution. Additionally, the conversation addresses proving that if a feasible solution exists, then any linear combination involving a vector d that satisfies certain conditions also remains feasible. The participants express uncertainty about how to approach the proofs, particularly in establishing the unboundedness of the problem. Overall, the thread emphasizes the complexities of linear programming and the need for clear justification in optimization scenarios.
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
 
Question: A clock's minute hand has length 4 and its hour hand has length 3. What is the distance between the tips at the moment when it is increasing most rapidly?(Putnam Exam Question) Answer: Making assumption that both the hands moves at constant angular velocities, the answer is ## \sqrt{7} .## But don't you think this assumption is somewhat doubtful and wrong?

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