Operations Research Project


Mastering of the basic techniques and methodologies related to the scientific sub-domain corresponding to the project. Development of creativity and critical sense, conducting to the formalization of a topic to be explored. Project development and conclusion.

General characterization





Responsible teacher

Ana Luísa da Graça Batista Custódio


Weekly - 2

Total - 28

Teaching language





C. Audet and W. Hare, Derivative-Free and Blackbox Optimization, Springer, 2017

J. Nocedal and S. J. Wright, Numerical Optimization (2nd ed.), Springer Series in Operations Research and Financial Engineering, 2006

A. L. Custódio, J. F. A. Madeira, A. I. F. Vaz, and L. N. Vicente, Direct multisearch for multiobjective optimization, SIAM J. Optim., 21 (2011), pp. 1109–1140

Numerical benchmark of algorithms:

E. D. Dolan and J. J. Moré, Benchmarking optimization software with performance profiles, Math. Program., 91 (2002), pp. 201–213

N. Gould and J. Scott, A note on performance profiles for benchmarking software, ACM Transactions on Mathmatical Software, 43 (2016), pp. 1–5

J.J. Moré and S.M. Wild, Benchmarking derivative-free optimization algorithms, SIAM J. Optim., 20 (2009), pp. 172–191

Teaching method

At the beginning of the semester each student will be allocated to a project topic. The student will have three weeks to be familiar with the subject and to prepare a short presentation about it, with the proposal of some points to be further explored. A brainstorming session will be conducted about these proposals, enrolling all students in the curricular unit and the professor.

After the approval of the theme to be explored, the student will be allowed to develop it during seven weeks, presenting oral progress reports in a weekly basis. Project supervision will be conducted in these weekly sessions.

Evaluation method


Subject matter

The final program will depend on the features of the project to be developed. However, there will be a common denominator related to scientific writing and oral presentations, as well as tools for numerical analysis of data (performance profiles and data profiles).


Programs where the course is taught: