You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We completely characterise the complexity in the decision tree model of computing composite relations of the form h = g(f^1,...,f^n), where each relation f^i is boolean-valued. Immediate corollaries include a direct sum theorem for decision tree complexity and a tight characterisation of the decision tree complexity of iterated boolean functions.

Cited Abstract(s):
[1]: A Direct Sum Theorem holds in a model of computation, when for every problem solving some k input instances together is k times as expensive as solving one. We show that Direct Sum Theorems hold in the models of deterministic and randomized decision trees for all relations. We also note that a near optimal Direct Sum Theorem holds for quantum decision trees for boolean functions.
In particular, Jain, Klauck and Santha have shown [1] such a direct sum theorem for deterministic and randomised decision tree complexity. As discussed in [1] , previous work had dismissed this question as uninteresting, but the proof is not immediate. In the deterministic case, the result of [1] is proven by showing that, given a decision tree @math computing @math independent outputs, one can produce @math independent trees @math , one for each output, and lower bounding the depth of @math in terms of the depth of the trees @math . This approach does not seem suitable for proving a composition theorem, as given a tree computing @math , it is not necessarily possible to produce individual trees for @math . On the other hand, the composition theorem given here in fact implies a direct sum theorem for decision tree complexity (see Section for this and other corollaries).