293 research outputs found
An Algorithm for Optimal Bipartite PLA Folding
This paper presents some results of PLA area optimizing by means of its column and row folding. A
more restricted type of PLA simple folding is considered. It is introduced by Egan and Liu and called as
bipartite folding. An efficient approach is presented which allows finding an optimal bipartite folding without
exhaustive computational efforts
Polyhedral characteristics of balanced and unbalanced bipartite subgraph problems
We study the polyhedral properties of three problems of constructing an
optimal complete bipartite subgraph (a biclique) in a bipartite graph. In the
first problem we consider a balanced biclique with the same number of vertices
in both parts and arbitrary edge weights. In the other two problems we are
dealing with unbalanced subgraphs of maximum and minimum weight with
nonnegative edges. All three problems are established to be NP-hard. We study
the polytopes and the cone decompositions of these problems and their
1-skeletons. We describe the adjacency criterion in 1-skeleton of the polytope
of the balanced complete bipartite subgraph problem. The clique number of
1-skeleton is estimated from below by a superpolynomial function. For both
unbalanced biclique problems we establish the superpolynomial lower bounds on
the clique numbers of the graphs of nonnegative cone decompositions. These
values characterize the time complexity in a broad class of algorithms based on
linear comparisons
On the orderability problem for PLA folding
AbstractAn interesting graph theoretic problem, called the orderability problem for programmable logic array (PLA) folding, was formulated and shown to be NP-complete. We show that a closely related problem, called the orderability problem for bipartite folding, is solvable in linear time
Pseudoknots in a Homopolymer
After a discussion of the definition and number of pseudoknots, we reconsider
the self-attracting homopolymer paying particular attention to the scaling of
the number of pseudoknots at different temperature regimes in two and three
dimensions. Although the total number of pseudoknots is extensive at all
temperatures, we find that the number of pseudoknots forming between the two
halves of the chain diverges logarithmically at (in both dimensions) and below
(in 2d only) the theta-temparature. We later introduce a simple model that is
sensitive to pseudoknot formation during collapse. The resulting phase diagram
involves swollen, branched and collapsed homopolymer phases with transitions
between each pair.Comment: submitted to PR
Towards Effective Exact Algorithms for the Maximum Balanced Biclique Problem
The Maximum Balanced Biclique Problem (MBBP) is a prominent model with
numerous applications. Yet, the problem is NP-hard and thus computationally
challenging. We propose novel ideas for designing effective exact algorithms
for MBBP. Firstly, we introduce an Upper Bound Propagation procedure to
pre-compute an upper bound involving each vertex. Then we extend an existing
branch-and-bound algorithm by integrating the pre-computed upper bounds. We
also present a set of new valid inequalities induced from the upper bounds to
tighten an existing mathematical formulation for MBBP. Lastly, we investigate
another exact algorithm scheme which enumerates a subset of balanced bicliques
based on our upper bounds. Experiments show that compared to existing
approaches, the proposed algorithms and formulations are more efficient in
solving a set of random graphs and large real-life instances
A Tutorial on Clique Problems in Communications and Signal Processing
Since its first use by Euler on the problem of the seven bridges of
K\"onigsberg, graph theory has shown excellent abilities in solving and
unveiling the properties of multiple discrete optimization problems. The study
of the structure of some integer programs reveals equivalence with graph theory
problems making a large body of the literature readily available for solving
and characterizing the complexity of these problems. This tutorial presents a
framework for utilizing a particular graph theory problem, known as the clique
problem, for solving communications and signal processing problems. In
particular, the paper aims to illustrate the structural properties of integer
programs that can be formulated as clique problems through multiple examples in
communications and signal processing. To that end, the first part of the
tutorial provides various optimal and heuristic solutions for the maximum
clique, maximum weight clique, and -clique problems. The tutorial, further,
illustrates the use of the clique formulation through numerous contemporary
examples in communications and signal processing, mainly in maximum access for
non-orthogonal multiple access networks, throughput maximization using index
and instantly decodable network coding, collision-free radio frequency
identification networks, and resource allocation in cloud-radio access
networks. Finally, the tutorial sheds light on the recent advances of such
applications, and provides technical insights on ways of dealing with mixed
discrete-continuous optimization problems
Efficient Generation of Stable Planar Cages for Chemistry
In this paper we describe an algorithm which generates all colored planar
maps with a good minimum sparsity from simple motifs and rules to connect them.
An implementation of this algorithm is available and is used by chemists who
want to quickly generate all sound molecules they can obtain by mixing some
basic components.Comment: 17 pages, 7 figures. Accepted at the 14th International Symposium on
Experimental Algorithms (SEA 2015
Efficient Mapping of Neural Network Models on a Class of Parallel Architectures.
This dissertation develops a formal and systematic methodology for efficient mapping of several contemporary artificial neural network (ANN) models on k-ary n-cube parallel architectures (KNC\u27s). We apply the general mapping to several important ANN models including feedforward ANN\u27s trained with backpropagation algorithm, radial basis function networks, cascade correlation learning, and adaptive resonance theory networks. Our approach utilizes a parallel task graph representing concurrent operations of the ANN model during training. The mapping of the ANN is performed in two steps. First, the parallel task graph of the ANN is mapped to a virtual KNC of compatible dimensionality. This involves decomposing each operation into its atomic tasks. Second, the dimensionality of the virtual KNC architecture is recursively reduced through a sequence of transformations until a desired metric is optimized. We refer to this process as folding the virtual architecture. The optimization criteria we consider in this dissertation are defined in terms of the iteration time of the algorithm on the folded architecture. If necessary, the mapping scheme may utilize a subset of the processors of a given KNC architecture if it results in the most efficient simulation. A unique feature of our mapping is that it systematically selects an appropriate degree of parallelism leading to a highly efficient realization of the ANN model on KNC architectures. A novel feature of our work is its ability to efficiently map unit-allocating ANN\u27s. These networks possess a dynamic structure which grows during training. We present a highly efficient scheme for simulating such networks on existing KNC parallel architectures. We assume an upper bound on size of the neural network We perform the folding such that the iteration time of the largest network is minimized. We show that our mapping leads to near-optimal simulation of smaller instances of the neural network. In addition, based on our mapping no data migration or task rescheduling is needed as the size of network grows
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