rae-miranda
Uploaded by
30 SLIDES
447 VUES
310LIKES

Enhancing ILP Solutions with Gomory Cuts: A Practical Example

DESCRIPTION

This document provides a comprehensive overview of Gomory cuts in the context of Integer Linear Programming (ILP). It includes a detailed walkthrough of the Gomory cutting plane algorithm, showcasing how these cuts can effectively strengthen linear programming relaxation. The example, drawn from Hamdy A. Taha's "Operations Research: An Introduction," illustrates the process and derivation of Gomory cuts using the simplex method. Key concepts include optimal ILP solutions, graphical solutions, and the significance of Gomory cuts in restricting feasible regions to improve outcomes.

1 / 30

Télécharger la présentation

Enhancing ILP Solutions with Gomory Cuts: A Practical Example

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Gomory Cuts Updated 25 March 2009

  2. Example ILP Example taken from “Operations Research: An Introduction” by Hamdy A. Taha (8th Edition)

  3. Example ILP in Standard Form

  4. Linear Programming Relaxation

  5. LP Relaxation: Final Tableau

  6. Row 1 Equation for x2 Every feasible ILP solution satisfies this constraint. Cuts off the continuous LP optimum (4.5, 3.5).

  7. Row 2 Equation for x1

  8. Row 2 Equation for x1

  9. Row 2 Equation for x1 Every feasible ILP solution satisfies this constraint. Cuts off the continuous LP optimum (4.5, 3.5).

  10. Equation for z

  11. Equation for z Every feasible ILP solution satisfies this constraint. Cuts off the continuous LP optimum (4.5, 3.5).

  12. General Form of Gomory Cuts

  13. General Form of Gomory Cuts Integer Part Fractional Part

  14. General Form of Gomory Cuts Integer Part Gomory Cut Fractional Part For each variable xi, ci is an integer and 0 fi< 1. On the right-hand side, I is an integer and 0 < f < 1.

  15. Comments on Gomory Cuts • Also called fractional cuts • Assume all variables are integer and non-negative • Apply to pure integer linear programs with integer coefficients • Strengthen linear programming relaxation of ILP by restricting the feasible region • “Outline of an algorithm for integer solutions to linear programs” by Ralph E. Gomory. Bull. Amer. Math. Soc. Volume 64, Number 5 (1958), 275-278.

  16. Cutting Plane Algorithm for ILP • Solve LP Relaxation with the Simplex Method • Until Optimal Solution is Integral Do • Derive a Gomory cut from the Simplex tableau • Add cut to tableau • Use a Dual Simplex pivot to move to a feasible solution

  17. Cutting Plane Algorithm Example: Cut 1

  18. Cutting Plane Algorithm Example: Cut 1

  19. Dual Simplex Method • Select a basic variable with a negative value in the RHS column to leave the basis • Let r be the row selected in Step 1 • Select a non-basic variable j to enter the basis such that • The entry in row r of column j, arj, is negative • The ratio -a0j /arj is minimized • Pivot on entry in row r of column j.

  20. Cutting Plane Algorithm Example: Cut 1

  21. Cutting Plane Algorithm Example: Cut 1

  22. Cutting Plane Algorithm Example: Cut 2

  23. Cutting Plane Algorithm Example: Cut 2

  24. Cutting Plane Algorithm Example: Cut 2

  25. Cutting Plane Algorithm Example: Cut 2

  26. Cutting Plane Algorithm Example: Cut 2

  27. Cutting Plane Algorithm Example: Cut 2 Optimal ILP Solution: x1 = 4, x2 = 3, and z =58

  28. LP Relaxation: Graphical Solution x2 4 Optimal Solution: (4.5, 3.5) 3 2 1 x1 1 2 3 4 5

  29. LP Relaxation with Cut 1 x2 4 3 Optimal Solution: (4 4/7, 3) 2 1 x1 1 2 3 4 5

  30. LP Relaxation with Cuts 1 and 2 x2 4 3 Optimal Solution: (4, 3) 2 1 x1 1 2 3 4 5

More Related