Consider the consensus problem of minimizing $f(x)=\sum_{i=1}^n f_i(x)$, where $x\in{\mathbb{R}}^p$ and each $f_i$ is only known to the individual agent $i$ in a connected network of $n$ agents. To solve this problem and obtain the solution, all the agents collaborate with their neighbors through information exchange. This type of decentralized computation does not need a fusion center, offers better network load balance, and improves data privacy. This paper studies the decentralized gradient descent method [A. Nedic and A. Ozdaglar, IEEE Trans. Automat. Control, 54 (2009), pp. 48--61], in which each agent $i$ updates its local variable $x_{(i)}\in{\mathbb{R}}^n$ by combining the average of its neighbors' with a local negative-gradient step $-\alpha \nabla f_i(x_{(i)})$. The method is described by the iteration $ x_{(i)}(k+1) \gets \sum_{j=1}^n w_{ij} x_{(j)}(k) - \alpha \nabla f_i(x_{(i)}(k)),\ \text{for each agent}~i, $ where $w_{ij}$ is nonzero only if $i$ and $j$ are neighbors or $i=j$ and the matrix...