Discrete Mathematics

Combinatorics, graph theory, logic, and number theory for computer science.


foundation tier

Discrete Mathematics addresses combinatorics, graph theory, logic, and number theory for computer science. It sits within Theoretical Foundations and inherits that area’s core questions about correctness, scale, and tractability. This page surveys the conceptual axes of the topic and points to the references that frame ongoing research and teaching. The intent is to be useful both as an entry point for newcomers and as an index for practitioners cross-checking their mental model against the field’s primary sources.

Work on discrete mathematics can be organised around a few interlocking concerns: the formal objects under study, the algorithms or systems that compute over them, the resource trade-offs (time, memory, communication, statistical efficiency), and the empirical or theoretical guarantees that practitioners rely on. The sources cited below approach the topic from a mix of these angles.

Foundational references

Rosen, Discrete Mathematics and Its Applications (2018) is a standard reference for this material and is used both as a curriculum anchor and as a long-form survey of techniques. Graham, Concrete Mathematics (1994) is a standard reference for this material and is used both as a curriculum anchor and as a long-form survey of techniques.

Open methodological questions in discrete mathematics cluster around how to compose the techniques above under realistic constraints — scale, adversarial inputs, partial observability, and shifting workloads. The cited references give the precise statements, proofs, and empirical evaluations that this overview only sketches; downstream topic pages drill into specific subfields.

Prerequisites

Sources

  • textbook · primary · 2018
    Discrete Mathematics and Its Applications
    rosen-2018
  • textbook · primary · 1994
    Concrete Mathematics
    graham-1994

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Explore

  1. 01

    Propositional Logic

    Boolean connectives, truth tables, normal forms, and proof systems.

  2. 02

    Predicate Logic

    First-order quantification, models, and soundness/completeness.

  3. 03

    Set Theory for CS

    Sets, relations, functions, and cardinality at the level needed for computer science.

  4. 04

    Combinatorics

    Counting, permutations, combinations, and generating functions.

  5. 05

    Graph Theory

    Graphs, trees, connectivity, matchings, colorings, and planarity.

  6. 06

    Spectral Graph Theory

    Eigenvalues of graph Laplacians and adjacency matrices and their structural implications.

  7. 07

    Probability for CS

    Discrete probability, concentration, and randomization tools used in algorithms.

  8. 08

    Number Theory for CS

    Modular arithmetic, primes, and algorithmic number theory.


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