Information
Requirements
Grading
- 80%- jeles
- 70-79% jó
- 60-69% közepes
- 50-59% elégséges
- 0-49% elégtelen
Themes
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1. Lecture
Introduction to mathematical modeling I - LP: product mix example. Slides for the 1st lecture
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2. Lecture
Basics of algorithmization, greedy algorithm. Slides for the 2nd lecture
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3. Lecture
Introduction to mathematical modeling II - maximization and minimization example, graphical method - feasible solutions. Slides for the 3rd lecture
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4. Lecture
Standard form, dictionary, introduction to the simplex algorithm. Slides for the 4th lecture
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5. Lecture
Simplex algorithm, classical and other pivot rules. Slides for the 5th lecture
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6. Lecture
Simplex algorithm special cases: alternative optimum, unboundedness, degeneracy. Slides for the 6th lecture
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7. Lecture
No feasible starting dictionary, introduction to the two-phase simplex method. Slides for the 7th lecture
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8. Lecture
Two-phase simplex method, fundamental theorem of linear programming, number of optimal solutions. Slides for the 8th lecture
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9. Lecture
Summary, examples. Slides for the 9th lecture
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10. Lecture
Introduction to the assignment problem, simple Hungarian method. Slides for the 10th lecture
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11. Lecture
Basics of integer programming, introduction to the branch and bound method. Slides for the 11th lecture
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12. Lecture
Knapsack problem, greedy algorithm for fractional knapsack, branch and bound. Slides for the 12th lecture
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13. Lecture
Optimization in general: continuous-discrete, linear-nonlinear, examples. Slides for the 13th lecture
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14. Lecture
Summary, examples. Slides for the 14th lecture