Operations Research and Optimization
Public syllabus for 2025-2026
Academic overview
Teaching team
Learning time distribution
| Total | ||||||
|---|---|---|---|---|---|---|
| Curriculum | Lecture | Practice | Total Weekly | Lecture | Practice | |
| 42 | 28 | 14 | 3 | 2 | 1 | |
| Exam hours | ||||||
| 0 | ||||||
| Individual Study | Bibliography study | Field study | Homework | Tutoring | Others | |
| 108 | 58 | 0 | 50 | 0 | 0 | |
| Overall | ||||||
| 150 |
Learning outcomes
Knowledge
- Knowledge, understanding, and practical application of concepts related to linear and nonlinear optimization techniques;
- Understanding how the computational complexity of an algorithm is determined and the specific requirements of scalability;
- Understanding how decision models can be designed based on data.
Skills
- Application of concepts from computer science, mathematics, and statistics in defining models and designing strategies for data analysis and interpretation of results;
- Use of knowledge regarding the construction of data-driven models to develop decision-support systems specific to various application domains.
Responsibility
- Responsibility for actions in accordance with users’ interests;
- Accurate representation of one’s level of competence and acceptance of tasks within its limits;
- Continuous professional development related to the practice of the profession.
Online platform
Course content
| Content | Methods | Obs |
|---|---|---|
| Introduction to optimization | online | 2h |
| Optimization Problems. Examples. Complexity of optimization problems. | online | 2h |
| Linear programming. Standard forms of an LP. Geometric interpretation. The structure of the space of solutions. Basic feasible solutions. | online | 2h |
| Finding a basic feasible solution. The two-stage Simplex algorithm. | online | 2h |
| The Simplex algorithm. Degeneration.Practical aspects. Bland’s rule (2 cursuri) | online | 4h |
| Totally unimodular matrices: characterizations and applications. | online | 2h |
| Dualitty in LP problems. | online | 2h |
| Solving ILP problems. The branch-and-bound method. | online | 2h |
| Solving ILP problems: Cutting planes | online | 2h |
| Advanced aspects: separation oracles și column generation. The cutting stock problem (2 cursuri) | online | 4h |
| Danzig-Wolfe decomposition. | online | 2h |
| Practical implementation aspects. | online | 2h |
Course bibliography
2. Wolsey, Laurence A., and George L. Nemhauser. Integer and combinatorial optimization. John Wiley & Sons, 2014. 3. Note de curs: Anupam Gupta and Ryan O'Donnell (CMU) Linear and Semidefinite Programming (Advanced Algorithms) Fall 2011 4. H.S. Gan si K. Akartunah, 620-462 Integer programming lecture notes. Note de lectura. Pdf va fi disponibil pe platforma Moodle. 5. Other online materials.
Seminar content
| Content | Methods | Obs |
|---|---|---|
| Solving theoretical problems related to the course topics | Exercises will involve developing models for LP/ILP problems. | 8h |
| Solving practical problems related to the course topics | Some of the algorithms presented in the lectures will be implemented. LP/ILP problems will be solved using Python/PuLP and Julia/JuMP. | 6h |
| Bibliography: The same as above. |
Seminar bibliography
The course is included in the programs of several prestigious universities worldwide. The topics have been, of course, adapted to local interests and to the mathematical level of the master’s students.
Corroboration
(none)
AI tools guidance
Evaluation and delivery
| Activity | Criteria | Methods | Percentage |
|---|---|---|---|
| C |
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| S |
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Performance standards
Basic knowledge of linear programming: canonical forms, writing the dual, the simplex algorithm. Basic knowledge of implementing linear/integer programming problems in Python/PuLP.
Additional info
(none)