Zobrazeno 1 - 10
of 238
pro vyhledávání: '"Shi, Jonathan"'
We present the first iterative spectral algorithm to find near-optimal solutions for a random quadratic objective over the discrete hypercube, resolving a conjecture of Subag [Subag, Communications on Pure and Applied Mathematics, 74(5), 2021]. The a
Externí odkaz:
http://arxiv.org/abs/2408.02360
Autor:
Sandhu, Juspreet Singh, Shi, Jonathan
We introduce a class of distributions which may be considered as a smoothed probabilistic version of the ultrametric property that famously characterizes the Gibbs distributions of various spin glass models. This class of \emph{high-entropy step} (HE
Externí odkaz:
http://arxiv.org/abs/2401.14383
Publikováno v:
Engineering, Construction and Architectural Management, 2023, Vol. 31, Issue 9, pp. 3557-3574.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/ECAM-12-2022-1196
Publikováno v:
14th Innovations in Theoretical Computer Science Conference (ITCS 2023); Article No. 77
We study random constraint satisfaction problems (CSPs) in the unsatisfiable regime. We relate the structure of near-optimal solutions for any Max-CSP to that for an associated spin glass on the hypercube, using the Guerra-Toninelli interpolation fro
Externí odkaz:
http://arxiv.org/abs/2210.03006
Publikováno v:
Proceedings of the Thirty-Second Conference on Learning Theory. PMLR. p. 1683--1722. 2019
We give a spectral algorithm for decomposing overcomplete order-4 tensors, so long as their components satisfy an algebraic non-degeneracy condition that holds for nearly all (all but an algebraic set of measure $0$) tensors over $(\mathbb{R}^d)^{\ot
Externí odkaz:
http://arxiv.org/abs/2203.02790
We introduce a notion of \emph{generic local algorithm} which strictly generalizes existing frameworks of local algorithms such as \emph{factors of i.i.d.} by capturing local \emph{quantum} algorithms such as the Quantum Approximate Optimization Algo
Externí odkaz:
http://arxiv.org/abs/2108.06049
We prove that a random $d$-regular graph, with high probability, is a cut sparsifier of the clique with approximation error at most $\left(2\sqrt{\frac 2 \pi} + o_{n,d}(1)\right)/\sqrt d$, where $2\sqrt{\frac 2 \pi} = 1.595\ldots$ and $o_{n,d}(1)$ de
Externí odkaz:
http://arxiv.org/abs/2008.05648
Publikováno v:
In Energy & Buildings 1 June 2023 288
Publikováno v:
In Computers & Industrial Engineering September 2022 171
Publikováno v:
In Technological Forecasting & Social Change August 2022 181