Duality Theory in Operations Research: Primal-Dual Relationships and Applications, Study notes of Systems Engineering

The concepts of duality theory in operations research, including the weak and strong duality properties, complementary solutions property, and symmetry property. The document also covers the complementary basic solutions property, complementary slackness property, and relationships between primal and dual solutions. Examples and problem-solving techniques, as well as applications of duality theory in various fields.

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Pre 2010

Uploaded on 11/08/2009

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Viterbi School of Engineering
Daniel J. Epstein Department of Industrial and Systems Engineering
ISE 330: Introduction to Operations Research
Fall 2006 (Oct 2): Duality Theory
http://www-scf.usc.edu/~ise330
Read: H&L chapter 6.3-6.4
Review: Duality Theory
Weak duality property: If x is a feasible solution for the primal problem and y is a feasible
solution for the dual problem, then cx yb.
Strong duality property: If x* is an optimal solution for the primal problem and y* is an
optimal solution for the dual problem, then cx* = y*b.
Complementary solutions property: At each iteration, the simplex method simultaneously
identifies a CPF solution x for the primal "roblem and a complementary
solution y for the dual problem (found in row 0, the coeff of the slack
variables), where cx = yb. If x is not optimal for the primal, y is infeasible.
Complementary optimal solutions property: At the final iteration, the simplex method
simultaneously identifies an optimal solution x* for the primal problem and a
complementary optimal solution y* for the dual problem (found in row 0,
the coeff of the slack variables), where cx* = y*b. In this solution, y* gives
the shadow prices for the primal problem.
Symmetry property: The dual of the dual is the primal.
Find the corresponding complementary basic solutions based on the simplex tableaux:
Final
Basic
Coeff
Tableaux
Var
Z
original
slack
RHS
Z
1
cBB-1A – c
cBB-1
cBB-1b
xB
0
B-1A
B-1
B-1b
pf3
pf4
pf5

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Viterbi School of Engineering

Daniel J. Epstein Department of Industrial and Systems Engineering

ISE 330: Introduction to Operations Research

Fall 2006 (Oct 2): Duality Theory

http://www-scf.usc.edu/~ise Read: H&L chapter 6.3-6. Review: Duality Theory Weak duality property: If x is a feasible solution for the primal problem and y is a feasible solution for the dual problem, then cxyb. Strong duality property: If x * is an optimal solution for the primal problem and y * is an optimal solution for the dual problem, then cx * = y * b. Complementary solutions property: At each iteration, the simplex method simultaneously identifies a CPF solution x for the primal "roblem and a complementary solution y for the dual problem (found in row 0, the coeff of the slack variables), where cx = yb. If x is not optimal for the primal, y is infeasible. Complementary optimal solutions property: At the final iteration, the simplex method simultaneously identifies an optimal solution x * for the primal problem and a complementary optimal solution y * for the dual problem (found in row 0, the coeff of the slack variables), where cx * = y * b. In this solution, y * gives the shadow prices for the primal problem. Symmetry property: The dual of the dual is the primal. Find the corresponding complementary basic solutions based on the simplex tableaux: Final Basic Coeff Tableaux Var Z original slack RHS Z 1 cBB-1A – c cBB-1^ cBB -1 b x B 0 B-1A B-1^ B-1b

Complementary basic solutions property: Each basic solution in the primal problem has a complementary basic solution in the dual problem , where their respective objective function values (Z and W) are equal. Complementary optimal basic solutions property: Each optimal basic solution in the primal problem has a complementary optimal basic solution in the dual problem, where their respective objective function values (Z and W ) are equal. Complementary slackness property: The variables in the primal basic solution and the complementary dual basic solution satisfy the complementary slackness as shown: Primal variable Dual variable Basic Non-basic (m variables) Non-basic Basic (n variables) Futhermore, the relationship is symmetric. Check for the complementary slackness property: Basic Coeff Iteration Var Z x 1 x 2 x 3 x 4 x 5 RHS 0 Z 1 -30 -15 0 0 0 0 x 3 0 1 0 1 0 0 4 x 4 0 0 2 0 1 0 12 x 5 0 3 2 0 0 1 18 1 Z 1 0 -15 30 0 0 120 x 1 0 1 0 1 0 0 4 x 4 0 0 2 0 1 0 12 x 5 0 0 2 -3 0 1 6 2 Z 1 0 0 7.5 0 7.5 165 x 1 0 1 0 1 0 0 4 x 4 0 0 0 3 1 -1 6 x 2 0 0 1 -1.5 0 0.5 3

Shortcut for conversion between primal and dual: Primal Dual Max Z Min W constraint i: ≤ = ≥ variable i: yi ≥ 0 variable xj: xj ≥ 0 constraint j: Examle: Quidditch Problem Max – Z = – 0.4 x 1 – 0.5 x 2 s.t. 0.3 x 1 + 0.1 x 2 ≤ 2. 0.5 x 1 + 0.5 x 2 = 6 0.6 x 1 + 0.4 x 2 ≥ 6 x 1 ≥ 0, x 2 ≥ 0

APPLICATIONS:

  • The dual problem can be solved directly to identify an opt soln for the primal.
  • If x is a feasible soln, and y a feasible soln for the dual is found easily such that cx = yb , then what can we say about x?
  • Use in the dual simplex method.
  • Plays a central role in sensitivity analysis.
  • Use in the economic interpretation of the dual problem and the resulting insights for analyzing primal. Special request: two problems similar to 5.2-2 and 5.3-5. 5.2-4 Work through the revised simplex method step by step to solve the model given in Prob. 4.1-5: Max Z = x 1 + 2 x 2 s.t. x 1 + 3 x 2 ≤ 8 x 1 + x 2 ≤ 4 x 1 ≥ 0, x 2 ≥ 0