Algorithmic and Discrete Mathematics

Courses on Algorithmic and Discrete Mathematics courses at SMA

(last modified June 18th 2020)

Contact person: Professor Friedrich Eisenbrand 

Bachelor 1-st year

MATH-260 Discrete Mathematics (2+2) 

Bachelor 2-nd year 

MATH-261 Discrete Optimization  (2+2)

Bachelor 3-rd year  

MATH-360 Graph Theory (2+2)
CS-250 Algorithms (4+2)
MATH-329 Nonlinear optimization (2+2)

During the first and second year of bachelor studies, the students are requested to follow the courses MATH-260 (mandatory) and MATH-261. Students who wish to pursue studies in algorithmic mathematics should then follow the course on Algorithms CS-250 and those who wish to specialize in combinatorics, should follow Graph Theory MATH-360. The student will then be able to successfully complete a semester project in applied algorithmic math, discrete optimization, combinatorics or discrete geometry. 


CS-450 Advanced Algorithms (4+3)
CS-433 Pattern Classification and Machine Learning (4+2)
MATH-461 Convexity (not always given) MATH-455 Combinatorial Statistics (2+2)
MATH-467 Probabilistic method in combinatorics (2+2)
MATH-504 Integer Optimization (2+2)
MATH-513 Metric Embeddings (2+2)
MATH-512 Optimization on Manifolds (2+2)
MATH-463 Mathematical modelling of behavior
CS-439 Optimization for Machine Learning

Students who want to specialize in algorithmic mathematics or optimization are requested to follow one of the courses CS-450 or CS-433. The course on Convexity MATH-461 is a specialization in structural results that are necessary to derive efficient algorithms for geometric and discrete geometric optimization problems. MATH-467 covers probabilistic methods in combinatorics. These techniques are also very useful in algorithms. The course on Combinatorial Statistics contains random graph theory, and the rest is stochastic block models, information propagation in graphs and some selected topics (some spectral graph theory and this year most likely epidemics models).

Some couses may not be given every academic year. Please refer to the official study plan

The CS courses are not in the official plan of math. Please contact once you are registered such that we can move the course in the right group. Mention that you are following the Algorithmic and DM track!

Related courses in Mathematics

Probabilités (MATH-230), Statistique (MATH-240): Many fundamental algorithmic techniques rely on insights and methods from probability and statistics. These courses treat the foundations of these fields. 
Statistical Theory (MATH-442): Statistics is the foundation of many algorithms for machine learning. A solid knowledge of statistical theory is very useful for those who want to specialize in algorithms for learning. 
Analyse numérique, knowledge on basic numerical methods is an important complement to discrete and convex methods.