We study the robustness of complete languages in PSPACE and prove that they are robust against P-selective sparse sets. Earlier similar results are known for EXP-complete sets [3] and NP-complete sets [7].

The many types of resource bounded reductions that are both object of study and research tool in structural complexity theory have given rise to a large variety of completeness notions. A complete set in a complexity class is a manageable object that represents the structure of the entire class

We study several properties of sets that are complete for NP. We prove that if L is an NP-complete set and S � ⊇ L is a p-selective sparse set, then L − S is ≤p m-hard for NP. We demonstrate existence of a sparse set S ∈ DTIME(22n) such that for every L ∈ NP − P, L − S is not ≤p m-hard for NP

We show that a set is m-autoreducible if and only if it is m-mitotic. This solves a long standing open question in a surprising way. As a consequence of this unconditional result and recent work by Gla6Ÿer et al., complete sets for all of the following complexity classes are m-mitotic: NP, coNP, â

We show that a set is m-autoreducible if and only if it is m-mitotic. This solves a long standing open question in a surprising way. As a consequence of this unconditional result and recent work by Glaßer et al. [11], complete sets for all of the following complexity classes are m-mitotic: NP, co

. We show that there is a set which is almost complete but not complete under polynomial-time many-one (p-m) reductions for the class E of sets computable in deterministic time 2 lin . Here a set A in a complexity class C is almost complete for C under some reducibility r if the class

This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem, and arises in many important applications as in the task

We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M. Can we complete the matrix and recover the entries that we have not seen? We show that one can

, better documentation, easier installation, improved portability, and higher performance. This report contains a complete description of the tool set, including retrieval and installation instructions, a description of how to use the tools, a description of the target SimpleScalar architecture, and many

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