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Electronic Colloquium on Computational Complexity

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REPORTS > KEYWORD > APPROXIMABILITY:
Reports tagged with approximability:
TR96-015 | 12th December 1995
Edoardo Amaldi, Viggo Kann

On the approximability of some NP-hard minimization problems for linear systems

We investigate the computational complexity of two classes of
combinatorial optimization problems related to linear systems
and study the relationship between their approximability properties.
In the first class (MIN ULR) one wishes, given a possibly infeasible
system of linear relations, to find ... more >>>


TR97-003 | 29th January 1997
Sanjeev Arora, Madhu Sudan

Improved low-degree testing and its applications


NP = PCP(log n, 1) and related results crucially depend upon
the close connection between the probability with which a
function passes a ``low degree test'' and the distance of
this function to the nearest degree d polynomial. In this
paper we study a test ... more >>>


TR99-038 | 27th August 1999
Peter Jonsson, Paolo Liberatore

On the Complexity of Finding Satisfiable Subinstances in Constraint Satisfaction

We study the computational complexity of an optimization
version of the constraint satisfaction problem: given a set $F$ of
constraint functions, an instance consists of a set of variables $V$
related by constraints chosen from $F$ and a natural number $k$. The
problem is to decide whether there exists a ... more >>>


TR00-054 | 5th May 2000
Andrea E. F. Clementi, Paolo Penna, Riccardo Silvestri

On the power assignment problem in radio networks

Given a finite set $S$ of points (i.e. the stations of a radio
network) on a $d$-dimensional Euclidean space and a positive integer
$1\le h \le |S|-1$, the \minrangeh{d} problem
consists of assigning transmission ranges to the stations so as
to minimize the total power consumption, provided ... more >>>


TR10-031 | 4th March 2010
Christian Glaßer, Christian Reitwießner, Heinz Schmitz, Maximilian Witek

Hardness and Approximability in Multi-Objective Optimization

We systematically study the hardness and the approximability of combinatorial multi-objective NP optimization problems (multi-objective problems, for short).

We define solution notions that precisely capture the typical algorithmic tasks in multi-objective optimization. These notions inherit polynomial-time Turing reducibility from multivalued functions, which allows us to compare the solution notions and ... more >>>


TR11-163 | 2nd December 2011
Libor Barto, Marcin Kozik

Robust Satisfiability of Constraint Satisfaction Problems

An algorithm for a constraint satisfaction problem is called robust if it outputs an assignment satisfying at least $(1-g(\varepsilon))$-fraction of the constraints given a $(1-\varepsilon)$-satisfiable instance, where $g(\varepsilon) \rightarrow 0$ as $\varepsilon \rightarrow 0$, $g(0)=0$.
Guruswami and Zhou conjectured a characterization of constraint languages for which the corresponding constraint satisfaction ... more >>>


TR12-078 | 14th June 2012
Vikraman Arvind, Sebastian Kuhnert, Johannes Köbler, Yadu Vasudev

Approximate Graph Isomorphism

We study optimization versions of Graph Isomorphism. Given two graphs $G_1,G_2$, we are interested in finding a bijection $\pi$ from $V(G_1)$ to $V(G_2)$ that maximizes the number of matches (edges mapped to edges or non-edges mapped to non-edges). We give an $n^{O(\log n)}$ time approximation scheme that for any constant ... more >>>


TR12-111 | 5th September 2012
Venkatesan Guruswami, Ali Kemal Sinop

Faster SDP hierarchy solvers for local rounding algorithms

Convex relaxations based on different hierarchies of
linear/semi-definite programs have been used recently to devise
approximation algorithms for various optimization problems. The
approximation guarantee of these algorithms improves with the number
of {\em rounds} $r$ in the hierarchy, though the complexity of solving
(or even writing down the solution for) ... more >>>


TR16-185 | 18th November 2016
Vijay Bhattiprolu, Mrinalkanti Ghosh, Venkatesan Guruswami, Euiwoong Lee, Madhur Tulsiani

Multiplicative Approximations for Polynomial Optimization Over the Unit Sphere

Revisions: 1

We consider the following basic problem: given an $n$-variate degree-$d$ homogeneous polynomial $f$ with real coefficients, compute a unit vector $x \in \mathbb{R}^n$ that maximizes $|f(x)|$. Besides its fundamental nature, this problem arises in many diverse contexts ranging from tensor and operator norms to graph expansion to quantum information ... more >>>


TR18-097 | 15th May 2018
Vijay Bhattiprolu, Mrinalkanti Ghosh, Venkatesan Guruswami, Euiwoong Lee, Madhur Tulsiani

Approximating Operator Norms via Generalized Krivine Rounding

We consider the $(\ell_p,\ell_r)$-Grothendieck problem, which seeks to maximize the bilinear form $y^T A x$ for an input matrix $A \in {\mathbb R}^{m \times n}$ over vectors $x,y$ with $\|x\|_p=\|y\|_r=1$. The problem is equivalent to computing the $p \to r^\ast$ operator norm of $A$, where $\ell_{r^*}$ is the dual norm ... more >>>




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