© 2019 Society for Industrial and Applied Mathematics We prove that with high probability over the choice of a random graph G from the Erd\H os-Rényi distribution G(n, 1/2), the nO(d)-time degree d sum-of-squares (SOS) semidefinite programming relaxation for the clique problem will give a value of at least n1/2 - c(d/ log n)1/2 for some constant c > 0. This yields a nearly tight n1/2 - o(1) bound on the value of this program for any degree d = o(log n). Moreover, we introduce a new framework that we call pseudocalibration to construct SOS lower bounds. This framework is inspired by taking a computational analogue of Bayesian probability theory. It yields a general recipe for constructing good pseudodistributions (i.e., dual certificates for...
We introduce a method for proving bounds on the SoS rank based on Boolean Function Analysis and Appr...
Semidenite programming (SDP) relaxations have been a popular choice for approximationalgorithm desig...
In combinatorial optimization, many problems can be modeled by optimizing a linear functional over ...
We prove that with high probability over the choice of a random graph G from the Erds-Rényi distribu...
We design new polynomial-time algorithms for recovering planted cliques in the semi-random graph mod...
Given a large data matrix A ∈ Rn×n, we consider the problem of determining whether its entries are i...
We study the Sum-of-Squares semidefinite programming hierarchy via the lens of average-case problems...
The Sum of Squares (SOS) algorithm (Parrilo, Lasserre) is a powerful convex programming hierarchy th...
We prove that the degree 4 sum-of-squares (SOS) relaxation of the clique number of the Paley graph o...
We present a general approach to rounding semidefinite programming relaxations obtained by the Sum-o...
In this paper, we construct general machinery for proving Sum-of-Squares lower bounds on certificati...
Convex relaxations are a central tool in modern algorithm design, but mathematically analyzingthe pe...
This paper proposes three new analytical lower bounds on the clique number of a graph and compares t...
This thesis studies two NP-complete problems, {\it Clique} and {\it Boolean Satisfiability} (SAT), u...
Despite significant successes in understanding the hardness of computational problems based on stand...
We introduce a method for proving bounds on the SoS rank based on Boolean Function Analysis and Appr...
Semidenite programming (SDP) relaxations have been a popular choice for approximationalgorithm desig...
In combinatorial optimization, many problems can be modeled by optimizing a linear functional over ...
We prove that with high probability over the choice of a random graph G from the Erds-Rényi distribu...
We design new polynomial-time algorithms for recovering planted cliques in the semi-random graph mod...
Given a large data matrix A ∈ Rn×n, we consider the problem of determining whether its entries are i...
We study the Sum-of-Squares semidefinite programming hierarchy via the lens of average-case problems...
The Sum of Squares (SOS) algorithm (Parrilo, Lasserre) is a powerful convex programming hierarchy th...
We prove that the degree 4 sum-of-squares (SOS) relaxation of the clique number of the Paley graph o...
We present a general approach to rounding semidefinite programming relaxations obtained by the Sum-o...
In this paper, we construct general machinery for proving Sum-of-Squares lower bounds on certificati...
Convex relaxations are a central tool in modern algorithm design, but mathematically analyzingthe pe...
This paper proposes three new analytical lower bounds on the clique number of a graph and compares t...
This thesis studies two NP-complete problems, {\it Clique} and {\it Boolean Satisfiability} (SAT), u...
Despite significant successes in understanding the hardness of computational problems based on stand...
We introduce a method for proving bounds on the SoS rank based on Boolean Function Analysis and Appr...
Semidenite programming (SDP) relaxations have been a popular choice for approximationalgorithm desig...
In combinatorial optimization, many problems can be modeled by optimizing a linear functional over ...