88,217 research outputs found
Unification and Logarithmic Space
We present an algebraic characterization of the complexity classes Logspace
and NLogspace, using an algebra with a composition law based on unification.
This new bridge between unification and complexity classes is inspired from
proof theory and more specifically linear logic and Geometry of Interaction.
We show how unification can be used to build a model of computation by means
of specific subalgebras associated to finite permutations groups. We then prove
that whether an observation (the algebraic counterpart of a program) accepts a
word can be decided within logarithmic space. We also show that the
construction can naturally represent pointer machines, an intuitive way of
understanding logarithmic space computing
Unification and Logarithmic Space
We present an algebraic characterization of the complexity classes Logspace
and Nlogspace, using an algebra with a composition law based on unification.
This new bridge between unification and complexity classes is rooted in proof
theory and more specifically linear logic and geometry of interaction. We show
how to build a model of computation in the unification algebra and then, by
means of a syntactic representation of finite permutations in the algebra, we
prove that whether an observation (the algebraic counterpart of a program)
accepts a word can be decided within logarithmic space. Finally, we show that
the construction naturally corresponds to pointer machines, a convenient way of
understanding logarithmic space computation.Comment: arXiv admin note: text overlap with arXiv:1402.432
Unification and Logarithmic Space: Journal Version
Soumis au numéro spécial de LMCS pour RTA/TLCA 2014 ( http://www.lmcs-online.org/ojs/specialIssues.php?id=67 )We present an algebraic characterization of the complexity classes Logspace and NLogspace, using an algebra with a composition law based on unification. This new bridge between unification and complexity classes is rooted in proof theory and more specifically linear logic and geometry of interaction. We show how to build a model of computation in the unification algebra and then, by means of a syntactic representation of finite permutations in the algebra, we prove that whether an observation (the algebraic counterpart of a program) accepts a word can be decided within logarithmic space. Finally, we show that the construction naturally corresponds to pointer machines, an convenient way of understanding logarithmic space computation
On the Algebraic Proof Complexity of Tensor Isomorphism
The Tensor Isomorphism problem (TI) has recently emerged as having connections to multiple areas of research within complexity and beyond, but the current best upper bound is essentially the brute force algorithm. Being an algebraic problem, TI (or rather, proving that two tensors are non-isomorphic) lends itself very naturally to algebraic and semi-algebraic proof systems, such as the Polynomial Calculus (PC) and Sum of Squares (SoS). For its combinatorial cousin Graph Isomorphism, essentially optimal lower bounds are known for approaches based on PC and SoS (Berkholz & Grohe, SODA \u2717). Our main results are an ?(n) lower bound on PC degree or SoS degree for Tensor Isomorphism, and a nontrivial upper bound for testing isomorphism of tensors of bounded rank.
We also show that PC cannot perform basic linear algebra in sub-linear degree, such as comparing the rank of two matrices (which is essentially the same as 2-TI), or deriving BA = I from AB = I. As linear algebra is a key tool for understanding tensors, we introduce a strictly stronger proof system, PC+Inv, which allows as derivation rules all substitution instances of the implication AB = I ? BA = I. We conjecture that even PC+Inv cannot solve TI in polynomial time either, but leave open getting lower bounds on PC+Inv for any system of equations, let alone those for TI. We also highlight many other open questions about proof complexity approaches to TI
Descent methods for Nonnegative Matrix Factorization
In this paper, we present several descent methods that can be applied to
nonnegative matrix factorization and we analyze a recently developped fast
block coordinate method called Rank-one Residue Iteration (RRI). We also give a
comparison of these different methods and show that the new block coordinate
method has better properties in terms of approximation error and complexity. By
interpreting this method as a rank-one approximation of the residue matrix, we
prove that it \emph{converges} and also extend it to the nonnegative tensor
factorization and introduce some variants of the method by imposing some
additional controllable constraints such as: sparsity, discreteness and
smoothness.Comment: 47 pages. New convergence proof using damped version of RRI. To
appear in Numerical Linear Algebra in Signals, Systems and Control. Accepted.
Illustrating Matlab code is included in the source bundl
Shattered Sets and the Hilbert Function
We study complexity measures on subsets of the boolean hypercube and exhibit connections between algebra (the Hilbert function) and combinatorics (VC theory). These connections yield results in both directions. Our main complexity-theoretic result demonstrates that a large and natural family of linear program feasibility problems cannot be computed by polynomial-sized constant-depth circuits. Moreover, our result applies to a stronger regime in which the hyperplanes are fixed and only the directions of the inequalities are given as input to the circuit. We derive this result by proving that a rich class of extremal functions in VC theory cannot be approximated by low-degree polynomials. We also present applications of algebra to combinatorics. We provide a new algebraic proof of the Sandwich Theorem, which is a generalization of the well-known Sauer-Perles-Shelah Lemma.
Finally, we prove a structural result about downward-closed sets, related to the Chvatal conjecture in extremal combinatorics
On Sequences, Rational Functions and Decomposition
Our overall goal is to unify and extend some results in the literature
related to the approximation of generating functions of finite and infinite
sequences over a field by rational functions. In our approach, numerators play
a significant role. We revisit a theorem of Niederreiter on (i) linear
complexities and (ii) ' minimal polynomials' of an infinite sequence,
proved using partial quotients. We prove (i) and its converse from first
principles and generalise (ii) to rational functions where the denominator need
not have minimal degree. We prove (ii) in two parts: firstly for geometric
sequences and then for sequences with a jump in linear complexity. The basic
idea is to decompose the denominator as a sum of polynomial multiples of two
polynomials of minimal degree; there is a similar decomposition for the
numerators. The decomposition is unique when the denominator has degree at most
the length of the sequence. The proof also applies to rational functions
related to finite sequences, generalising a result of Massey. We give a number
of applications to rational functions associated to sequences.Comment: Several more typos corrected. To appear in J. Applied Algebra in
Engineering, Communication and Computing. The final publication version is
available at Springer via http://dx.doi.org/10.1007/s00200-015-0256-
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