Chandra-Furst-Lipton

4 Upper bound on the deterministic communication complexity of \(\operatorname{eval}\)

Definition 4.1 The non-monochromatic protocol

Given a coloring \(c : \{ x \mid \operatorname{eval}x = 1\} \to [C]\), writing \(a_i\) the vector whose \(j\)-th coordinate is \(x_j\) except when \(j = i\) in which case it is \(-\sum _{j \ne i} x_j\), we define the non-monochromatic protocol for \(c\) by making participant \(i\) do “Send the \(t / d\)-th bit of \(c(a_i)\) until time \(\lceil \log _2\chi _d(G)\rceil \), then send \(1\) iff \(c(a_i)\) agrees with the broadcast from time \(1\) to time \(\lceil \log _2\chi _d(G)\rceil \) read as a color” and “Send \(1\) iff the broadcasts from time \(\lceil \log _2\chi _d(G)\rceil \) to time \(\lceil \log _2\chi _d(G)\rceil + d\) were all \(1\)”.

Lemma 4.2 The non-monochromatic protocol is valid

If \(c\) is a coloring such that all monochromatic forbidden patterns are trivial, then the non-monochromatic protocol for \(c\) is valid in time \(\lceil \log _2\chi _d(G)\rceil + d\).

Proof

We have

\[ \text{answer is } 1 \iff \text{ all $a_i$ have the same color } \iff \text{ all $a_i$ are equal } \iff \sum _i x_i = 0 \]

where the first equivalence holds by definition, the second one holds by assumption and the third one holds since the \(a_i\) form a forbidden pattern.

Theorem 4.3 Upper bound for \(D(\operatorname{eval})\) in terms of \(\chi _d(G)\)
\[ D(\operatorname{eval}) \le \lceil \log _2\chi _d(G)\rceil + d \]
Proof

Using Lemma 3.2, find some coloring \(c\) of \(\{ x \mid \sum _i x_i = 0\} \) in \(\chi _d(G)\) colors such that all monochromatic forbidden patterns are trivial. Then Lemma 4.2 tells us that the non-monochromatic protocol for \(c\) is valid in time \(\lceil \log _2\chi _d(G)\rceil + d\).

Corollary 4.4 Upper bound for \(D(\operatorname{eval})\) in terms of \(r_d(G)\)
\[ D(\operatorname{eval}) \le 2d\log _2\frac N{r_d(G)} \]
Proof

Putting Theorem 4.3 and Lemma 1.7 together, we get

\[ D(\operatorname{eval}) \le \left\lceil \log _2\frac{2dN^d\log N}{r_d(G)}\right\rceil \le 2d\log _2\frac N{r_d(G)} \]