ﻻ يوجد ملخص باللغة العربية
We consider a very wide class of models for sparse random Boolean 2CSPs; equivalently, degree-2 optimization problems over~${pm 1}^n$. For each model $mathcal{M}$, we identify the high-probability value~$s^*_{mathcal{M}}$ of the natural SDP relaxation (equivalently, the quantum value). That is, for all $varepsilon > 0$ we show that the SDP optimum of a random $n$-variable instance is (when normalized by~$n$) in the range $(s^*_{mathcal{M}}-varepsilon, s^*_{mathcal{M}}+varepsilon)$ with high probability. Our class of models includes non-regular CSPs, and ones where the SDP relaxation value is strictly smaller than the spectral relaxation value.
We precisely determine the SDP value (equivalently, quantum value) of large random instances of certain kinds of constraint satisfaction problems, ``two-eigenvalue 2CSPs. We show this SDP value coincides with the spectral relaxation value, possibly i
Unlike its cousin 3SAT, the NAE-3SAT (not-all-equal-3SAT) problem has the property that spectral/SDP algorithms can efficiently refute random instances when the constraint density is a large constant (with high probability). But do these methods work
We show, assuming the Strong Exponential Time Hypothesis, that for every $varepsilon > 0$, approximating directed Diameter on $m$-arc graphs within ratio $7/4 - varepsilon$ requires $m^{4/3 - o(1)}$ time. Our construction uses nonnegative edge weight
We consider the problem of scattering $n$ robots in a two dimensional continuous space. As this problem is impossible to solve in a deterministic manner, all solutions must be probabilistic. We investigate the amount of randomness (that is, the numbe
The problem of finding a common refinement of a set of rooted trees with common leaf set $L$ appears naturally in mathematical phylogenetics whenever poorly resolved information on the same taxa from different sources is to be reconciled. This consti