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The decomposition theory of matroids initiated by Paul Seymour in the 1980s has had an enormous impact on research in matroid theory. This theory, when applied to matrices over the binary field, yields a powerful decomposition theory for binary linear codes. In this paper, we give an overview of this code decomposition theory, and discuss some of its implications in the context of the recently discovered formulation of maximum-likelihood (ML) decoding of a binary linear code over a binary-input discrete memoryless channel as a linear programming problem. We translate matroid-theoretic results of Grotschel and Truemper from the combinatorial optimization literature to give examples of nontrivial families of codes for which the ML decoding problem can be solved in time polynomial in the length of the code. One such family is that consisting of codes for which the codeword polytope is identical to the Koetter-Vontobel fundamental polytope derived from the entire dual code Cperp. However, we also show that such families of codes are not good in a coding-theoretic sense-either their dimension or their minimum distance must grow sublinearly with code length. As a consequence, we have that decoding by linear programming, when applied to good codes, cannot avoid failing occasionally due to the presence of pseudocode words.