Acyclic Chromatic Number Calculator

no bichromatic cycles

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About Acyclic Chromatic Number Calculator

An acyclic chromatic number calculator computing a(G): minimum proper coloring where every 2-colored subgraph is acyclic (forest). a(G) ≥ χ(G). Planar: a ≤ 5 (Borodin 1979). Every graph: a ≤ d²/2 where d = max degree. Client-side.

Acyclic Chromatic Number Calculator Features

  • a(G) value
  • vs χ
  • No 2-color cycle
  • Planar ≤ 5
  • Common graphs
Acyclic chromatic number a(G): minimum colors for proper coloring where every pair of color classes induces a forest (acyclic). Stronger than proper coloring. Borodin (1979): planar → a ≤ 5. Grünbaum conjecture proved! Applications in Hessian computation.

How to Use

Select graph:

  • a: Acyclic χ
  • Check: No 2-color cycle
  • vs χ: Compare

Borodin's Theorem

Every planar graph has a(G) ≤ 5. Proves Grünbaum's 1973 conjecture. The proof is intricate, using discharging. Tight: some planar graphs need a = 5. One of the jewels of graph coloring theory.

Hessian Computation

Computing sparse Hessian matrices: acyclic coloring determines the number of matrix-vector products needed. a(G) colors → Hessian computable in a(G) gradient evaluations. Important for optimization, machine learning.

Step-by-Step Instructions

  1. 1Select graph.
  2. 2Compute a(G).
  3. 3Check bichromatic cycles.
  4. 4Apply Borodin.
  5. 5Use for Hessian.

Acyclic Chromatic Number Calculator — Frequently Asked Questions

Why 'acyclic'?+

Every 2-colored subgraph (union of two color classes) must be acyclic (a forest). This prevents monochromatic and bichromatic cycles. Stronger than proper coloring where only monochromatic edges are forbidden.

What's the difference from star coloring?+

Star coloring: no 2-colored path of length 4. Acyclic coloring: no 2-colored cycle. Star coloring is strictly stronger: star coloring implies acyclic coloring, but not vice versa.

How is this used in optimization?+

Sparse Hessian computation: acyclic coloring of the adjacency graph determines grouping. Fewer colors → fewer gradient evaluations needed. Critical speedup for large-scale nonlinear optimization.

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