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Worked solution: Huang's signed hypercube adjacency matrix proof of the sensitivity conjecture (2019)

Step 1 of 7: The sensitivity conjecture and the cube reformulation
In plain words

A Boolean function ff takes nn bits of input and outputs one bit. Its sensitivity s(f)s(f) measures how many single-bit flips can change the output at a worst-case input, while its degree deg⁡(f)\deg(f) measures how complicated ff is when written as a polynomial. Nisan and Szegedy asked in 1992 whether a function with small s(f)s(f) must also have small deg⁡(f)\deg(f).

Δ(H)≥nfor every induced subgraph H⊆Qn with ∣V(H)∣≥2n−1+1\Delta(H) \ge \sqrt{n} \quad \text{for every induced subgraph } H \subseteq Q_n \text{ with } |V(H)| \ge 2^{n-1}+1
Detailed analysis

Huang (2019, Introduction) studies the nn-dimensional hypercube graph QnQ_n, whose 2n2^n vertices are the binary strings in {0,1}n\{0,1\}^n and whose edges join strings differing in exactly one coordinate. In 1988, Chung, Furedi, Graham and Seymour proved that every induced subgraph on more than half the vertices of QnQ_n has maximum degree at least (1/2−o(1))log⁡2n(1/2-o(1))\log_2 n, and they constructed an induced subgraph on exactly 2n−1+12^{n-1}+1 vertices whose maximum degree is only ⌈n⌉\lceil\sqrt n\rceil. Huang's paper closes the gap between these two bounds completely: Theorem 1.1 states that every induced subgraph HH of QnQ_n on at least 2n−1+12^{n-1}+1 vertices satisfies Δ(H)≥n\Delta(H) \ge \sqrt{n}, matching the earlier construction exactly.

Terms in this step
Boolean function
A function f:{0,1}n→{0,1}f : \{0,1\}^n \to \{0,1\} taking nn bits as input and producing one bit of output.
Sensitivity s(f)s(f)
The largest number of single-bit flips that change the value of ff at some input xx, maximised over all inputs xx.
Degree deg⁡(f)\deg(f)
Every Boolean function equals a unique multilinear real polynomial in its nn input bits; deg⁡(f)\deg(f) is the degree of that polynomial.
Induced subgraph
Given a vertex subset SS of a graph GG, the induced subgraph keeps exactly the edges of GG with both endpoints in SS.
Knowledge used in this step