260 research outputs found
Complexity of packing common bases in matroids
One of the most intriguing unsolved questions of matroid optimization is the
characterization of the existence of disjoint common bases of two matroids.
The significance of the problem is well-illustrated by the long list of
conjectures that can be formulated as special cases, such as Woodall's
conjecture on packing disjoint dijoins in a directed graph, or Rota's beautiful
conjecture on rearrangements of bases.
In the present paper we prove that the problem is difficult under the rank
oracle model, i.e., we show that there is no algorithm which decides if the
common ground set of two matroids can be partitioned into common bases by
using a polynomial number of independence queries. Our complexity result holds
even for the very special case when .
Through a series of reductions, we also show that the abstract problem of
packing common bases in two matroids includes the NAE-SAT problem and the
Perfect Even Factor problem in directed graphs. These results in turn imply
that the problem is not only difficult in the independence oracle model but
also includes NP-complete special cases already when , one of the matroids
is a partition matroid, while the other matroid is linear and is given by an
explicit representation.Comment: 14 pages, 9 figure
Mathematical Programming Decoding of Binary Linear Codes: Theory and Algorithms
Mathematical programming is a branch of applied mathematics and has recently
been used to derive new decoding approaches, challenging established but often
heuristic algorithms based on iterative message passing. Concepts from
mathematical programming used in the context of decoding include linear,
integer, and nonlinear programming, network flows, notions of duality as well
as matroid and polyhedral theory. This survey article reviews and categorizes
decoding methods based on mathematical programming approaches for binary linear
codes over binary-input memoryless symmetric channels.Comment: 17 pages, submitted to the IEEE Transactions on Information Theory.
Published July 201
Fractional Linear Matroid Matching is in quasi-NC
The matching and linear matroid intersection problems are solvable in
quasi-NC, meaning that there exist deterministic algorithms that run in
polylogarithmic time and use quasi-polynomially many parallel processors.
However, such a parallel algorithm is unknown for linear matroid matching,
which generalizes both of these problems. In this work, we propose a quasi-NC
algorithm for fractional linear matroid matching, which is a relaxation of
linear matroid matching and commonly generalizes fractional matching and linear
matroid intersection. Our algorithm builds upon the connection of fractional
matroid matching to non-commutative Edmonds' problem recently revealed by Oki
and Soma~(2023). As a corollary, we also solve black-box non-commutative
Edmonds' problem with rank-two skew-symmetric coefficients
Preprocessing Under Uncertainty: Matroid Intersection
We continue the study of preprocessing under uncertainty that was initiated independently by Assadi et al. (FSTTCS 2015) and Fafianie et al. (STACS 2016). Here, we are given an instance of a tractable problem with a large static/known part and a small part that is dynamic/uncertain, and ask if there is an efficient algorithm that computes an instance of size polynomial in the uncertain part of the input, from which we can extract an optimal solution to the original instance for all (usually exponentially many) instantiations of the uncertain part.
In the present work, we focus on the Matroid Intersection problem. Amongst others we present a positive preprocessing result for the important case of finding a largest common independent set in two linear matroids. Motivated by an application for intersecting two gammoids we also revisit Maximum Flow. There we tighten a lower bound of Assadi et al. and give an alternative positive result for the case of low uncertain capacity that yields a Maximum Flow instance as output rather than a matrix
FPT-Algorithms for the l-Matchoid Problem with Linear and Submodular Objectives
We design a fixed-parameter deterministic algorithm for computing a maximum
weight feasible set under a -matchoid of rank , parameterized by
and . Unlike previous work that presumes linear representativity of
matroids, we consider the general oracle model. Our result, combined with the
lower bounds of Lovasz, and Jensen and Korte, demonstrates a separation between
the -matchoid and the matroid -parity problems in the setting of
fixed-parameter tractability.
Our algorithms are obtained by means of kernelization: we construct a small
representative set which contains an optimal solution. Such a set gives us much
flexibility in adapting to other settings, allowing us to optimize not only a
linear function, but also several important submodular functions. It also helps
to transform our algorithms into streaming algorithms.
In the streaming setting, we show that we can find a feasible solution of
value and the number of elements to be stored in memory depends only on
and but totally independent of . This shows that it is possible to
circumvent the recent space lower bound of Feldman et al., by parameterizing
the solution value. This result, combined with existing lower bounds, also
provides a new separation between the space and time complexity of maximizing
an arbitrary submodular function and a coverage function in the value oracle
model
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Combinatorial Optimization
Combinatorial Optimization is a very active field that benefits from bringing together ideas from different areas, e.g., graph theory and combinatorics, matroids and submodularity, connectivity and network flows, approximation algorithms and mathematical programming, discrete and computational geometry, discrete and continuous problems, algebraic and geometric methods, and applications. We continued the long tradition of triannual Oberwolfach workshops, bringing together the best researchers from the above areas, discovering new connections, and establishing new and deepening existing international collaborations
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