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ISM 206 Lecture 4

ISM 206 Lecture 4. Duality and Sensitivity Analysis. Announcements. Homework 2 on web due Thursday 14th Lecture tonight 6pm. Outline. Sensitivity analysis: How does the solution change as the parameters change? How much would we ‘pay’ for more resources

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ISM 206 Lecture 4

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  1. ISM 206Lecture 4 Duality and Sensitivity Analysis

  2. Announcements Homework 2 on web due Thursday 14th Lecture tonight 6pm

  3. Outline • Sensitivity analysis: • How does the solution change as the parameters change? • How much would we ‘pay’ for more resources • What is the effect of changing parameters A, b, c • What is duality and why does it matter? • The dual of a linear program • Sensitivity through duality • Sensitivity through parametric LP solving

  4. The elements of the simplex tableu • After any iteration, the coefficients of the slack variables in each equation immediately reveal how that equation has been obtained from the initial equations. • The text talks about the ‘fundamental insight’: • After any iteration, the coefficients of the slack variables in each equation immediately reveal how that equation has been obtained by the initial equations

  5. The dual Linear Program Dual Primal

  6. Dual Linear Programs • The dual of a LP is another LP • Coefficients of primal objective = rhs of dual constraints • Rhs of primal constraints = coeffs of dual objective • Variable coefficients are the same (transposed)

  7. Translating between primal and dual • The dual of the dual is the primal • Weak duality theorem • Strong duality theorem • Complementary Slackness • Optimality Conditions • Interpretation of dual variables • Dual Simplex algorithm

  8. Questions and Break

  9. Sensitivity Analysis • Changes in b • Changes in c • Changes in A • Introduction of a new variable • Introduction of a new constraint • Parametric Linear Programming • All demonstrated in OR Tutor

  10. Changes in b • Handle by checking optimality conditions under previous basis • How much could b change and still be optimal? • ranging

  11. Changes in c • Change will not affect feasibility! • Different procedure when parameter being changed depends on basic or nonbasic variable • Called ranging again

  12. Introducing a New variable • Same as changing the coefficients to a nonbasic variable

  13. Introducing a New Constraint • Check feasibility of original optimal • Add row to tableu and proceed as

  14. Parametric LP • Are there values of the parameter for which the problem has a solution? • How do the objective and optimal x depend on the parameter?

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