Weizmann Logo
ECCC
Electronic Colloquium on Computational Complexity

Under the auspices of the Computational Complexity Foundation (CCF)

Login | Register | Classic Style



REPORTS > KEYWORD > LINEAR PROGRAMMING:
Reports tagged with linear programming:
TR03-081 | 10th October 2003
Valentin E. Brimkov, Bruno Codenotti, Valentino Crespi, Reneta Barneva, Mauro Leoncini

Computation of the Lov\'asz Theta Function for Circulant Graphs

The Lov\'asz theta function $\theta(G)$ of a graph $G$ has attracted a lot of attention for its connection with diverse issues, such as communicating without errors and computing large cliques in graphs. Indeed this function enjoys the remarkable property of being computable in polynomial time, despite being sandwitched between clique ... more >>>


TR05-156 | 13th December 2005
Jonathan A. Kelner, Daniel A. Spielman

A Randomized Polynomial-Time Simplex Algorithm for Linear Programming (Preliminary Version)

We present the first randomized polynomial-time simplex algorithm for linear programming. Like the other known polynomial-time algorithms for linear programming, its running time depends polynomially on the number of bits used to represent its input.

We begin by reducing the input linear program to a special form in which we ... more >>>


TR06-041 | 6th March 2006
Tomas Feder, Rajeev Motwani, An Zhu

k-connected spanning subgraphs of low degree

We consider the problem of finding a $k$-vertex ($k$-edge)
connected spanning subgraph $K$ of a given $n$-vertex graph $G$
while minimizing the maximum degree $d$ in $K$. We give a
polynomial time algorithm for fixed $k$ that achieves an $O(\log
n)$-approximation. The only known previous polynomial algorithms
achieved degree $d+1$ ... more >>>


TR06-132 | 17th October 2006
Grant Schoenebeck, Luca Trevisan, Madhur Tulsiani

Tight Integrality Gaps for Lovasz-Schrijver LP Relaxations of Vertex Cover and Max Cut

Revisions: 1

We study linear programming relaxations of Vertex Cover and Max Cut
arising from repeated applications of the ``lift-and-project''
method of Lovasz and Schrijver starting from the standard linear
programming relaxation.

For Vertex Cover, Arora, Bollobas, Lovasz and Tourlakis prove that
the integrality gap remains at least $2-\epsilon$ after
$\Omega_\epsilon(\log n)$ ... more >>>


TR10-180 | 18th November 2010
Gregory Valiant, Paul Valiant

Estimating the unseen: A sublinear-sample canonical estimator of distributions

We introduce a new approach to characterizing the unobserved portion of a distribution, which provides sublinear-sample additive estimators for a class of properties that includes entropy and distribution support size. Together with the lower bounds proven in the companion paper [29], this settles the longstanding question of the sample complexities ... more >>>


TR11-077 | 8th May 2011
Albert Atserias, Elitza Maneva

Graph Isomorphism, Sherali-Adams Relaxations and Expressibility in Counting Logics

Two graphs with adjacency matrices $\mathbf{A}$ and $\mathbf{B}$ are isomorphic if there exists a permutation matrix $\mathbf{P}$ for which the identity $\mathbf{P}^{\mathrm{T}} \mathbf{A} \mathbf{P} = \mathbf{B}$ holds. Multiplying through by $\mathbf{P}$ and relaxing the permutation matrix to a doubly stochastic matrix leads to the notion of fractional isomorphism. We show ... more >>>


TR15-171 | 28th October 2015
Joshua Grochow

Monotone projection lower bounds from extended formulation lower bounds

Revisions: 2 , Comments: 1

In this short note, we show that the permanent is not complete for non-negative polynomials in $VNP_{\mathbb{R}}$ under monotone p-projections. In particular, we show that Hamilton Cycle polynomial and the cut polynomials are not monotone p-projections of the permanent. To prove this we introduce a new connection between monotone projections ... more >>>


TR15-189 | 25th November 2015
Shay Moran, Cyrus Rashtchian

Shattered Sets and the Hilbert Function

Revisions: 1

We study complexity measures on subsets of the boolean hypercube and exhibit connections between algebra (the Hilbert function) and combinatorics (VC theory). These connections yield results in both directions. Our main complexity-theoretic result proves that most linear program feasibility problems cannot be computed by polynomial-sized constant-depth circuits. Moreover, our result ... more >>>


TR16-017 | 24th December 2015
Georgios Stamoulis

Limitations of Linear Programming Techniques for Bounded Color Matchings

Given a weighted graph $G = (V,E,w)$, with weight function $w: E \rightarrow \mathbb{Q^+}$, a \textit{matching} $M$ is a set of pairwise non-adjacent edges. In the optimization setting, one seeks to find a matching of \textit{maximum} weight. In the \textit{multi-criteria} (or \textit{multi-budgeted}) setting, we are also given $\ell$ length functions ... more >>>


TR17-106 | 16th June 2017
Mateus de Oliveira Oliveira, Pavel Pudlak

Representations of Monotone Boolean Functions by Linear Programs

We introduce the notion of monotone linear programming circuits (MLP circuits), a model of
computation for partial Boolean functions. Using this model, we prove the following results:

1. MLP circuits are superpolynomially stronger than monotone Boolean circuits.
2. MLP circuits are exponentially stronger than monotone span programs.
3. ... more >>>


TR18-059 | 6th April 2018
Joshua Brakensiek, Venkatesan Guruswami

Combining LPs and Ring Equations via Structured Polymorphisms

Revisions: 1

Promise CSPs are a relaxation of constraint satisfaction problems where the goal is to find an assignment satisfying a relaxed version of the constraints. Several well known problems can be cast as promise CSPs including approximate graph and hypergraph coloring, discrepancy minimization, and interesting variants of satisfiability. Similar to CSPs, ... more >>>


TR19-054 | 9th April 2019
Joshua Brakensiek, Venkatesan Guruswami

Bridging between 0/1 and Linear Programming via Random Walks

Under the Strong Exponential Time Hypothesis, an integer linear program with $n$ Boolean-valued variables and $m$ equations cannot be solved in $c^n$ time for any constant $c < 2$. If the domain of the variables is relaxed to $[0,1]$, the associated linear program can of course be solved in polynomial ... more >>>




ISSN 1433-8092 | Imprint