More space saving

1 files changed, 2 insertions, 2 deletions

diff --git a/approximation.tex b/approximation.tex
index 2747d5a..bab2c93 100755
--- a/approximation.tex
+++ b/approximation.tex
@@ -129,7 +129,7 @@ In other words, $f$ is $\delta$-decreasing if increasing any coordinate by $\del
 
 We achieve this by restricting the optimization over a subset of $\dom_c$ at which the concave relaxation $L$ is ``sufficiently'' concave. Formally, for $\alpha\geq 0$ let
 $$\textstyle\dom_{c,\alpha} \defeq \{\lambda \in [\alpha,1]^n: \sum_{i\in \mathcal{N}}c_i\lambda_i \leq B\}\subseteq  \dom_c . $$ 
-Note that $\dom_c=\dom_{c,0}.$ Consider the following perturbation of the concave relaxation \eqref{eq:primal}:
+Note that $\dom_c=\dom_{c,0}.$ Consider the following perturbed problem:
 \begin{align}\tag{$P_{c,\alpha}$}\label{eq:perturbed-primal}
 \begin{split}  \text{Maximize:} &\qquad L(\lambda)\\
 \text{subject to:} & \qquad\lambda \in \dom_{c,\alpha}
@@ -144,4 +144,4 @@ Note that $\dom_c=\dom_{c,0}.$ Consider the following perturbation of the concav
     $L^*_c$. The running time of the algorithm is $O\big(poly(n, d,
     \log\log\frac{B}{b\varepsilon\delta})\big)$.
 \end{proposition}
-The proof and the formal description of the algorithm are in \cite{arxiv}.
+The proof can be found in \cite{arxiv}.