1. CS5321 Numerical Optimization
13 Linear Programming: The Simplex Method
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2. Minxz=cTx subject to Ax = b, x  0
The standard form
The standard form of linear programming is
Minxz=cTx subject to Ax = b, x  0
Matrix A is anmn matrix, where m is the number of constraints and n is the number of variables.
We assume A has full row rank and m  n.
For Axb, add slack variables. Ax − s = b, s  0.
For Axb, subtract slack variables Ax + s=b, s  0.
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3. Geometry of LP Feasible region : the set of all feasible points
If  is empty, LP has no solution. (infeasible)
If  is nonempty, it is convex.
If the object function is unbounded on , LP has no solution.
If LP is bounded and feasible, it can have either one or infinitely many solutions.
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4. Basic feasible point
A point x is a basic feasible point (or a vertex of feasible polytope) if it is feasible and is not a linear combination of any other feasible points.
If LP has solutions, at least one solution is a basic feasible point. (Theorem 13.2)
At a basic feasible point, at least n−m variables are zero.
The case it has more than n−m zero variables is called degenerate.
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5. Basic variable and basis matrix
At a basic feasible point, variables that can be uniquely determined are called basic variables.
The other are called nonbasic variables. (set to zero.)
Let B, N be the index sets for basic/nonbasic variables.
Variable x, c, and A can be rearranged according to basic/nonbasic variables.
B, an nn nonsingular matrix, is called the basis matrix x = B N ; c A
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6. Simplex multiplier Since xN=0 at a basic feasible pointB N ; c A
Since xN=0 at a basic feasible point
Object function:
Constraints:
The basic variables are and therefore the object function
The simplex multiplier is z = c T x B + N A x = B + N b x B = 1 b z = c T B x 1 b = ( c T B 1 )
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7. Pricing
Object function could be decreased by changing nonbasic variables, xN. ,
Plug in ,
The vector is called pricing.
If sN(i)<0, z can be decrease by increasing xN(i).
If all sN(i)0, the optimal solution is founded. z = c T x B + N x B = 1 b N A x = B + N b z = c T B x + N 1 b ( ) = ( c T B 1 ) z = c T B 1 b + ( N ) x s N = c T
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8. The ratio test
Select q s.t. sN(q)<0 is the smallest element in sN and increase xN(q) until one element in xB, say xB(p), becomes zero. (How to find p?)
Need xB(i)  0 for all i. If B−1N(:,q)(i)<0, then xB(i)>0
Only need to consider i for B−1N(:,q)(i)0
What if no such i? the unbounded case x B = 1 b N ( p r e v i o u s l d . ) : ; q c t p = a r g m i n 1 : ( B b ) N ; q
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9. Pivoting Exchange p and q in B,N and update xB, xN and B. Let
Update xB = xB− d and xN(q) = 
Update B: replace B(p) with N(q) (How about B−1?)
It is a rank-1 update. Let B+ be the updated one.
The Sherman-Morrison formula d = ( B 1 N : ; q ) b p B + = ( N : ; q ) e p T ( B + ) 1 = N : ; q e p T
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10. The simplex method While (true)
Given B,N. xB=B−1b, xN = 0 (Basic feasible point)
=B−1cB, sN=cN−NT (Simplex multiplier, pricing)
If sN  0, stop (Found an optimal solution)
Select q s.t. sN(q)<0, and solve Bd=N(:,q)
If d  0, stop (Unbounded case)
Compute [,p]=mind(i)>0 xB(i)/d(i) (Ratio test)
Update xB=xB− d and xN(p) =  (Pivoting)
Exchange p and q in B,N and update matrix B.
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11. Remaining problems How to find the initial basic feasible point?
Two phase algorithm: add more slack variables to make the trivial point (0,…,0) feasible, and solve it until all additional slack variables become zero.
How to resolve the degenerate case?
In degenerate case, the algorithm might pivot the same p and q repeatedly.
Perturb the constraints to avoid the degenerate case.
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12. Complexity
In each iteration, the most time consuming task is pricing, ratio test and update B. O(mn)
The number of iterations is less than or equals to the number of basic feasible points, which is
The worst case time complexity is exponential.
Try n=2m. The number of iterations > 2m.
But practically, it terminates in m to 3m iterations.
Average case analysis and smoothed analysis. n m = ! ( )
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