The Unique Games Conjecture and optimal approximation thresholds

The following describes the scope of the Lean formalization related to the following accompanying paper(s):

• The Unique Games Theorem
• A Direct Proof of Optimal Max-Cut Hardness
• The Factor-Two Hardness Threshold for Vertex Cover
• Constant-factor hardness of Min-UnCut
• Constant-factor hardness of directed feedback vertex set

Scope

The Unique Games conjecture asks for hardness of distinguishing nearly satisfiable unique games from games of very small value. The formalized result gives, for every fixed $0<\varepsilon,\delta<1/2$, a deterministic polynomial-time reduction from binary 3SAT to nonempty unweighted simple bipartite unique games. Satisfiable inputs have value at least $1-\varepsilon$ and unsatisfiable inputs have value at most $\delta$. The alphabet is a fixed $\mathbb F_2^s$ depending only on the errors, and every constraint is a translation.

The formalized result establishes hardness of approximating Max-Cut beyond the Goemans–Williamson constant $\alpha_{\mathrm{GW}}$. For every fixed $\alpha_{\mathrm{GW}}<\alpha\le1$, it gives a deterministic polynomial-time reduction from binary 3SAT to a strictly separated Max-Cut gap on finite simple unweighted graphs. The reduction includes the explicit positive integer scaling used in the gap statement.

The formalized result gives the factor-two hardness threshold for Vertex Cover. For each integer $m\ge4$, a polynomial-time reduction from binary 3SAT produces simple unweighted graphs with cover density below $1/2+1/m$ on satisfiable inputs and above $1-1/m$ on unsatisfiable inputs. Consequently, any polynomial-time approximation with a fixed factor $1\le\alpha<2$ would give a polynomial-time decision algorithm for 3SAT. No assumption that $P\ne NP$ is built into the statement.

Min-UnCut minimizes the number of edges left uncut by a bipartition. The formalization constructs a deterministic reduction from encoded $3$-SAT formulas to finite simple unweighted Min-UnCut instances with arbitrarily large fixed multiplicative gaps. For every integer $K\ge2$, satisfiable formulas give optimum at most the output threshold, while unsatisfiable formulas give optimum strictly greater than $K$ times that threshold. Runtime and output length are polynomial for each fixed $K$, establishing hardness for every fixed approximation factor greater than one.

A directed feedback vertex set meets every directed cycle. The formalization proves hardness of approximating the minimum such set within any fixed constant factor. For every real factor $A\ge1$ and every language in NP, it constructs a polynomial-time gap reduction to unweighted directed graphs with the stated completeness and soundness separation. Thus a fixed-factor polynomial-time approximation would imply a polynomial-time algorithm for every NP language.

Comparator links