44,670 research outputs found
Feasible combinatorial matrix theory
We show that the well-known Konig's Min-Max Theorem (KMM), a fundamental
result in combinatorial matrix theory, can be proven in the first order theory
\LA with induction restricted to formulas. This is an
improvement over the standard textbook proof of KMM which requires
induction, and hence does not yield feasible proofs --- while our new approach
does. \LA is a weak theory that essentially captures the ring properties of
matrices; however, equipped with induction \LA is capable of
proving KMM, and a host of other combinatorial properties such as Menger's,
Hall's and Dilworth's Theorems. Therefore, our result formalizes Min-Max type
of reasoning within a feasible framework
Solving rank-constrained semidefinite programs in exact arithmetic
We consider the problem of minimizing a linear function over an affine
section of the cone of positive semidefinite matrices, with the additional
constraint that the feasible matrix has prescribed rank. When the rank
constraint is active, this is a non-convex optimization problem, otherwise it
is a semidefinite program. Both find numerous applications especially in
systems control theory and combinatorial optimization, but even in more general
contexts such as polynomial optimization or real algebra. While numerical
algorithms exist for solving this problem, such as interior-point or
Newton-like algorithms, in this paper we propose an approach based on symbolic
computation. We design an exact algorithm for solving rank-constrained
semidefinite programs, whose complexity is essentially quadratic on natural
degree bounds associated to the given optimization problem: for subfamilies of
the problem where the size of the feasible matrix is fixed, the complexity is
polynomial in the number of variables. The algorithm works under assumptions on
the input data: we prove that these assumptions are generically satisfied. We
also implement it in Maple and discuss practical experiments.Comment: Published at ISSAC 2016. Extended version submitted to the Journal of
Symbolic Computatio
Combinatorial simplex algorithms can solve mean payoff games
A combinatorial simplex algorithm is an instance of the simplex method in
which the pivoting depends on combinatorial data only. We show that any
algorithm of this kind admits a tropical analogue which can be used to solve
mean payoff games. Moreover, any combinatorial simplex algorithm with a
strongly polynomial complexity (the existence of such an algorithm is open)
would provide in this way a strongly polynomial algorithm solving mean payoff
games. Mean payoff games are known to be in NP and co-NP; whether they can be
solved in polynomial time is an open problem. Our algorithm relies on a
tropical implementation of the simplex method over a real closed field of Hahn
series. One of the key ingredients is a new scheme for symbolic perturbation
which allows us to lift an arbitrary mean payoff game instance into a
non-degenerate linear program over Hahn series.Comment: v1: 15 pages, 3 figures; v2: improved presentation, introduction
expanded, 18 pages, 3 figure
Product Multicommodity Flow in Wireless Networks
We provide a tight approximate characterization of the -dimensional
product multicommodity flow (PMF) region for a wireless network of nodes.
Separate characterizations in terms of the spectral properties of appropriate
network graphs are obtained in both an information theoretic sense and for a
combinatorial interference model (e.g., Protocol model). These provide an inner
approximation to the dimensional capacity region. These results answer
the following questions which arise naturally from previous work: (a) What is
the significance of in the scaling laws for the Protocol
interference model obtained by Gupta and Kumar (2000)? (b) Can we obtain a
tight approximation to the "maximum supportable flow" for node distributions
more general than the geometric random distribution, traffic models other than
randomly chosen source-destination pairs, and under very general assumptions on
the channel fading model?
We first establish that the random source-destination model is essentially a
one-dimensional approximation to the capacity region, and a special case of
product multi-commodity flow. Building on previous results, for a combinatorial
interference model given by a network and a conflict graph, we relate the
product multicommodity flow to the spectral properties of the underlying graphs
resulting in computational upper and lower bounds. For the more interesting
random fading model with additive white Gaussian noise (AWGN), we show that the
scaling laws for PMF can again be tightly characterized by the spectral
properties of appropriately defined graphs. As an implication, we obtain
computationally efficient upper and lower bounds on the PMF for any wireless
network with a guaranteed approximation factor.Comment: Revised version of "Capacity-Delay Scaling in Arbitrary Wireless
Networks" submitted to the IEEE Transactions on Information Theory. Part of
this work appeared in the Allerton Conference on Communication, Control, and
Computing, Monticello, IL, 2005, and the Internation Symposium on Information
Theory (ISIT), 200
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