Using usual notation we have,
$SDP(G) \geq OPT(G) \geq Alg_{GW}(G) \geq \alpha_{GW} SDP(G) \geq \alpha_{GW} OPT(G)$
where we mean,
$SDP(G)$ = The maximum value that the SDP finds of the objective function $\sum_{(u,v) \in E} w_{uv}( 1-\vec{y_u}.\vec{y_v})/2$ (one unit vector $\vec{y_v}$ in $\mathbb{R}^d$ for each vertex $v$ for some $d$) And $w_{uv}$ are the given edge weights such that $\sum_{(u,v) \in E} w_{uv} =1$
$OPT(G)$ = The actual size of the max-cut for the graph
$Alg_{GW}(G)$ = The value of the max-cut returned by the randomized rounding prescription of Goemans-Williamson
$\alpha_{GW}$ = the famous constant of $\sim 0.878$
If say someone shows that for every $\epsilon$ there is a graph $G(\epsilon)$ such that $(\alpha_{GW}+\epsilon)OPT(G(\epsilon)) \geq Alg_{GW}(G(\epsilon))$ then does this imply that this is a class of graphs on which the algorithm is performing as bad as it could?
Why does showing the existence of such a family of graphs as above also necessarily imply that on this family $SDP(G(\epsilon)) = OPT(G(\epsilon))$ ?