Concept

Graph homomorphism

Related lectures (19)

Information Theory: Basics

Covers the basics of information theory, entropy, and fixed points in graph colorings and the Ising model.

Probabilistic Methods in Combinatorics

Covers probabilistic methods in combinatorics, monochromatic edges, 2-colorable graphs, and good 2-colorings.

Covers register allocation techniques, including interference graph examples, coloring, spilling, and coalescing.

Belief Propagation in Random Graphs

Explores belief propagation in random graphs and Bethe free entropy.

Algorithmes: introduction

Covers the basics of algorithms, problem-solving, and efficient resolution methods.

Markov Chains and Algorithm Applications

Covers the application of Markov chains and algorithms for function optimization and graph colorings.

Linearity of Expectation: First Moment Method

Introduces Linearity of Expectation and the First Moment Method, explores probability theory problems like Buffon's Needle, and discusses transitive tournaments and Ham paths.

Probability Theory: Markov's Theorem

Explores Markov's theorem, Chernoff bound, and probability theory fundamentals, including good coloring, 2-colorable graphs, and rare events.

Dense Graphs: From Theory to Applications

Explores the transition from sparse to dense graphs and their real-world applications.

Statistical Analysis of Network Data: Structures and Models

Explores statistical analysis of network data, covering graph structures, models, statistics, and sampling methods.

Causal Inference: Learning Graph Structures

Explores causal inference through learning graph structures for causal reasoning from observational data.

Optimization: Register Allocation

Covers advanced compiler construction topics on optimization techniques like dead code elimination, constant folding, and register allocation.

Cayley Graphs

Covers Cayley graphs, generators, group examples, and graph structures.

Irreducible Matrices and Strong Connectivity

Explores irreducible matrices and strong connectivity in networked control systems, emphasizing the importance of adjacency matrices and graph structures.

Integral Calculus: Techniques and Applications

Explores integral calculus techniques, areas under graphs, Darboux sums, and the fundamental theorem of calculus.

Fixed Points in Graph Theory

Focuses on fixed points in graph theory and their implications in algorithms and analysis.

Untitled

Graph Theory and Network Flows

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.

The Nerve and Geometric Realization

Delves into the computation and geometric realization of small categories, exploring the relationship between nerves and geometric structures.