Optimization methods

Information

Requirements

Grading

• 80%- jeles
• 70-79% jó
• 60-69% közepes
• 50-59% elégséges
• 0-49% elégtelen

Themes

• 1. Lecture

  Introduction to mathematical modeling I - LP: product mix example. Slides for the 1st lecture

• 2. Lecture

  Basics of algorithmization, greedy algorithm. Slides for the 2nd lecture

• 3. Lecture

  Introduction to mathematical modeling II - maximization and minimization example, graphical method - feasible solutions. Slides for the 3rd lecture

• 4. Lecture

  Standard form, dictionary, introduction to the simplex algorithm. Slides for the 4th lecture

• 5. Lecture

  Simplex algorithm, classical and other pivot rules. Slides for the 5th lecture

• 6. Lecture

  Simplex algorithm special cases: alternative optimum, unboundedness, degeneracy. Slides for the 6th lecture

• 7. Lecture

  No feasible starting dictionary, introduction to the two-phase simplex method. Slides for the 7th lecture

• 8. Lecture

  Two-phase simplex method, fundamental theorem of linear programming, number of optimal solutions. Slides for the 8th lecture

• 9. Lecture

  Summary, examples. Slides for the 9th lecture

• 10. Lecture

  Introduction to the assignment problem, simple Hungarian method. Slides for the 10th lecture

• 11. Lecture

  Basics of integer programming, introduction to the branch and bound method. Slides for the 11th lecture

• 12. Lecture

  Knapsack problem, greedy algorithm for fractional knapsack, branch and bound. Slides for the 12th lecture

• 13. Lecture

  Optimization in general: continuous-discrete, linear-nonlinear, examples. Slides for the 13th lecture

• 14. Lecture

  Summary, examples. Slides for the 14th lecture