Lectures related to Untitled | EPFL Graph Search

Covers the basics of sets and operations in mathematics, from set properties to advanced operations.

Explores convexity, Jacobians, subdifferentials, and convergence rates in optimization and function analysis.

Covers the generalities of functions, including the definition of an application between sets and the uniqueness of elements in the image set.

Introduces Cartesian product and induction for proofs using integers and sets.

Introduces fundamental concepts of linear algebra, focusing on sets and operations.

Covers the properties and sets related to linear algebra, emphasizing the importance of understanding these concepts.

Explores associative operations on tuples and sets, demonstrating their properties through various examples and proofs.

Introduces sets in discrete mathematics and explores proof techniques like direct and indirect proofs.

Explores mapping functions, surjections, injective and surjective functions, and bijective functions.

Explores tuples, sets, and dictionaries in Python, covering immutability, uniqueness, and key-value pairs.

Covers examples of relation composition, Cartesian product, and injective functions.

Explores fixed points in G-sets, applications between sets, and the construction of orbits.

Covers sets in Scala, N-Queens problem, and recursive algorithms.

Covers the fundamental concepts of sets and subsets in linear algebra, including operations and properties.

Explores the Rockafellar Theorem in optimal transport, focusing on c-cyclical monotonicity and convex functions.

Covers the KKT conditions and convex optimization, discussing constraint qualifications and tangent cones of convex sets.

Explores geodesic convexity in metric spaces and its applications, discussing properties and the stability of inequalities.

Covers the Cartesian product of sets and binary relations with detailed examples and graphical representations.

Covers the concept of convex functions and their applications in optimization problems.

Explores VC dimension, gradient descent, convex sets, and Lipschitz functions in convex optimization.