## An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs

** M. Pawan Kumar, Vladimir Kolmogorov, Philip H.S. Torr**; 10(3):71−106, 2009.

### Abstract

The problem of obtaining the maximum *a posteriori* estimate of a
general discrete Markov random field (i.e., a Markov random field
defined using a discrete set of labels) is known to be
NP-hard. However, due to its central importance in many applications,
several approximation algorithms have been proposed in the
literature. In this paper, we present an analysis of three such
algorithms based on convex relaxations: (i) LP-S: the linear
programming (LP) relaxation proposed by Schlesinger (1976)
for a special case and independently in Chekuri et al. (2001),
Koster et al. (1998), and Wainwright et al. (2005) for the general case;
(ii) QP-RL: the quadratic programming (QP) relaxation of
Ravikumar and Lafferty (2006); and (iii) SOCP-MS: the second order
cone programming (SOCP) relaxation first proposed by
Muramatsu and Suzuki (2003) for two label problems and later extended by
Kumar et al. (2006) for a general label set.

We show that the SOCP-MS and the QP-RL relaxations are
equivalent. Furthermore, we prove that despite the flexibility in the
form of the constraints/objective function offered by QP and
SOCP, the LP-S relaxation *strictly dominates* (i.e.,
provides a better approximation than) QP-RL and SOCP-MS.
We generalize these results by defining a large class of SOCP
(and equivalent QP) relaxations which is dominated by the
LP-S relaxation. Based on these results we propose some novel
SOCP relaxations which define constraints using random variables that
form cycles or cliques in the graphical model representation of the
random field. Using some examples we show that the new SOCP
relaxations strictly dominate the previous approaches.

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