  This paper considers the optimization problem of the form $\min_{{\bf
x}\in{\mathbb R}^d} f({\bf x})\triangleq \frac{1}{n}\sum_{i=1}^n f_i({\bf x})$,
where $f(\cdot)$ satisfies the Polyak--{\L}ojasiewicz (PL) condition with
parameter $\mu$ and $\{f_i(\cdot)\}_{i=1}^n$ is $L$-mean-squared smooth. We
show that any gradient method requires at least
$\Omega(n+\kappa\sqrt{n}\log(1/\epsilon))$ incremental first-order oracle (IFO)
calls to find an $\epsilon$-suboptimal solution, where $\kappa\triangleq L/\mu$
is the condition number of the problem. This result nearly matches upper bounds
of IFO complexity for best-known first-order methods. We also study the problem
of minimizing the PL function in the distributed setting such that the
individuals $f_1(\cdot),\dots,f_n(\cdot)$ are located on a connected network of
$n$ agents. We provide lower bounds of
$\Omega(\kappa/\sqrt{\gamma}\,\log(1/\epsilon))$,
$\Omega((\kappa+\tau\kappa/\sqrt{\gamma}\,)\log(1/\epsilon))$ and
$\Omega\big(n+\kappa\sqrt{n}\log(1/\epsilon)\big)$ for communication rounds,
time cost and local first-order oracle calls respectively, where
$\gamma\in(0,1]$ is the spectral gap of the mixing matrix associated with the
network and~$\tau>0$ is the time cost of per communication round. Furthermore,
we propose a decentralized first-order method that nearly matches above lower
bounds in expectation.
