  In this paper we provide oracle complexity lower bounds for finding a point
in a given set using a memory-constrained algorithm that has access to a
separation oracle. We assume that the set is contained within the unit
$d$-dimensional ball and contains a ball of known radius $\epsilon>0$. This
setup is commonly referred to as the feasibility problem. We show that to solve
feasibility problems with accuracy $\epsilon \geq e^{-d^{o(1)}}$, any
deterministic algorithm either uses $d^{1+\delta}$ bits of memory or must make
at least $1/(d^{0.01\delta }\epsilon^{2\frac{1-\delta}{1+1.01 \delta}-o(1)})$
oracle queries, for any $\delta\in[0,1]$. Additionally, we show that randomized
algorithms either use $d^{1+\delta}$ memory or make at least $1/(d^{2\delta}
\epsilon^{2(1-4\delta)-o(1)})$ queries for any $\delta\in[0,\frac{1}{4}]$.
Because gradient descent only uses linear memory $\mathcal O(d\ln 1/\epsilon)$
but makes $\Omega(1/\epsilon^2)$ queries, our results imply that it is
Pareto-optimal in the oracle complexity/memory tradeoff. Further, our results
show that the oracle complexity for deterministic algorithms is always
polynomial in $1/\epsilon$ if the algorithm has less than quadratic memory in
$d$. This reveals a sharp phase transition since with quadratic $\mathcal O(d^2
\ln1/\epsilon)$ memory, cutting plane methods only require $\mathcal O(d\ln
1/\epsilon)$ queries.
