We construct near optimal linear decision trees for a variety of decision problems in combinatorics and discrete geometry.
For example, for any constant $k$, we construct linear decision trees that solve the $k$-SUM problem on $n$ elements using $O(n \log^2 n)$ linear queries.
Moreover, the queries we use are comparison ... more >>>

We introduce the notion of \emph{Min-Entropic Optimality} thereby providing a framework for arguing that a given algorithm computes a function better than any other algorithm. An algorithm is $k(n)$ Min-Entropic Optimal if for every distribution $D$ with min-entropy at least $k(n)$, its expected running time when its input is drawn ... more >>>

In the average-case $k$-SUM problem, given $r$ integers chosen uniformly at random from $\{0,\ldots,M-1\}$, the objective is to find a set of $k$ numbers that sum to $0$ modulo $M$ (this set is called a ``solution''). In the related $k$-XOR problem, given $k$ uniformly random Boolean vectors of length $\log{M}$, ... more >>>

The Subset Sum problem asks whether, given $n$ integers and a target, some subset of the integers sums to the target. Its best known worst-case running time is $O^*(2^{n/2})$ (Horowitz and Sahni, 1974), whereas the best quantum upper bound is $O^*(2^{n/3})$ (Bernstein, Jeffery, Lange, and Meurer, 2013). The $k$-SUM problem ... more >>>

We prove lower bounds for $k$-OV, $k$-XOR, and $k$-SUM in nonuniform AC$^0$, tracking how the circuit-size exponent scales with $k$ and using no running-time hypothesis. Our framework gives depth-zero projections from colored subgraph isomorphism to the three targets at dimension, row count, or bit width $O(k\log n)$, without increasing depth ... more >>>