Maximum Flow: Theory and Applications

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Related lectures (44)

Iterated Integrals: Order and Domains Log in to Mediaspace to watch this video

Explores iterated integrals, order, domains, and symmetrical roles of variables in 2D space.

Partial Derivatives: Understanding Tangents Log in to Mediaspace to watch this video

Explores partial derivatives and their role in determining tangents to graphs.

General Mathematics I: Derivatives and Tangents Log in to Mediaspace to watch this video

Explores derivatives, tangents, and elementary function rules in general mathematics.

Bipartite and n-partite Graphs Log in to Mediaspace to watch this video

Covers bipartite and n-partite graphs, including complete graph definitions and examples.

Level Curves and Graphs of Functions Log in to Mediaspace to watch this video

Covers level curves and graphs of functions in two and three dimensions.

Max-flow and Disjoint Sets Log in to Mediaspace to watch this video

Explores the Ford-Fulkerson method, max-flow, applications of max-flow, and the disjoint-set data structure.

Matplotlib: Overview Log in to Mediaspace to watch this video

Covers a wide range of topics in Matplotlib, including simple plots, subplots, and various types of charts.

Uniform Acceleration: Equations and Interpretation Log in to Mediaspace to watch this video

Covers the concept of uniform acceleration and its equations.

Max-flow Min-cut Theorem Log in to Mediaspace to watch this video

Explores the Max-flow Min-cut theorem, integral capacities, Ford-Fulkerson method, bipartite matching, and edge-disjoint paths.

Matplotlib: Types of Graphs Log in to Mediaspace to watch this video

Explores different types of graphs achievable with Matplotlib, showcasing a variety of visualization techniques.

Ford-Fulkerson Method: Max Flow and Min Cut Log in to Mediaspace to watch this video

Explores the Ford-Fulkerson method for finding max flow and min cut in a network.

Distances and Motif Counts Log in to Mediaspace to watch this video

Explores distances on graphs, cut norms, spanning trees, blockmodels, metrics, norms, and ERGMs in network data analysis.

Graph Algorithms: Modeling and Representation Log in to Mediaspace to watch this video

Covers the basics of graph algorithms, focusing on modeling and representation of graphs in memory.

Expander Graphs: Properties and Eigenvalues Log in to Mediaspace to watch this video

Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.

Graphical Models: Probability Distributions and Factor Graphs Log in to Mediaspace to watch this video

Covers graphical models for probability distributions and factor graphs representation.

Algorithmic Paradigms for Dynamic Graph Problems Log in to Mediaspace to watch this video

Covers algorithmic paradigms for dynamic graph problems, including dynamic connectivity, expander decomposition, and local clustering, breaking barriers in k-vertex connectivity problems.

Statistical analysis of network data Log in to Mediaspace to watch this video

Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.

Low Diameter Random Partitioning Log in to Mediaspace to watch this video

Discusses Low Diameter Randomized Decomposition and graph partitioning for edge cuts and coloring.

Cartesian Product: Graph Operations Log in to Mediaspace to watch this video

Explores the Cartesian product operation on graphs and introduces the concept of cubes.

Reformulating Problems: Tools and Intuition Log in to Mediaspace to watch this video

Focuses on open problems and the importance of reformulating problems with better tools and intuition.

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