30,956 research outputs found
Graph-Based Decoding in the Presence of ISI
We propose an approximation of maximum-likelihood detection in ISI channels
based on linear programming or message passing. We convert the detection
problem into a binary decoding problem, which can be easily combined with LDPC
decoding. We show that, for a certain class of channels and in the absence of
coding, the proposed technique provides the exact ML solution without an
exponential complexity in the size of channel memory, while for some other
channels, this method has a non-diminishing probability of failure as SNR
increases. Some analysis is provided for the error events of the proposed
technique under linear programming.Comment: 25 pages, 8 figures, Submitted to IEEE Transactions on Information
Theor
An Improved Algorithm for Fixed-Hub Single Allocation Problem
This paper discusses the fixed-hub single allocation problem (FHSAP). In this
problem, a network consists of hub nodes and terminal nodes. Hubs are fixed and
fully connected; each terminal node is connected to a single hub which routes
all its traffic. The goal is to minimize the cost of routing the traffic in the
network. In this paper, we propose a linear programming (LP)-based rounding
algorithm. The algorithm is based on two ideas. First, we modify the LP
relaxation formulation introduced in Ernst and Krishnamoorthy (1996, 1999) by
incorporating a set of validity constraints. Then, after obtaining a fractional
solution to the LP relaxation, we make use of a geometric rounding algorithm to
obtain an integral solution. We show that by incorporating the validity
constraints, the strengthened LP often provides much tighter upper bounds than
the previous methods with a little more computational effort, and the solution
obtained often has a much smaller gap with the optimal solution. We also
formulate a robust version of the FHSAP and show that it can guard against data
uncertainty with little cost
Logic Integer Programming Models for Signaling Networks
We propose a static and a dynamic approach to model biological signaling
networks, and show how each can be used to answer relevant biological
questions. For this we use the two different mathematical tools of
Propositional Logic and Integer Programming. The power of discrete mathematics
for handling qualitative as well as quantitative data has so far not been
exploited in Molecular Biology, which is mostly driven by experimental
research, relying on first-order or statistical models. The arising logic
statements and integer programs are analyzed and can be solved with standard
software. For a restricted class of problems the logic models reduce to a
polynomial-time solvable satisfiability algorithm. Additionally, a more dynamic
model enables enumeration of possible time resolutions in poly-logarithmic
time. Computational experiments are included
An optimal bifactor approximation algorithm for the metric uncapacitated facility location problem
We obtain a 1.5-approximation algorithm for the metric uncapacitated facility
location problem (UFL), which improves on the previously best known
1.52-approximation algorithm by Mahdian, Ye and Zhang. Note, that the
approximability lower bound by Guha and Khuller is 1.463.
An algorithm is a {\em (,)-approximation algorithm} if
the solution it produces has total cost at most , where and are the facility and the connection
cost of an optimal solution. Our new algorithm, which is a modification of the
-approximation algorithm of Chudak and Shmoys, is a
(1.6774,1.3738)-approximation algorithm for the UFL problem and is the first
one that touches the approximability limit curve
established by Jain, Mahdian and Saberi. As a consequence, we obtain the first
optimal approximation algorithm for instances dominated by connection costs.
When combined with a (1.11,1.7764)-approximation algorithm proposed by Jain et
al., and later analyzed by Mahdian et al., we obtain the overall approximation
guarantee of 1.5 for the metric UFL problem. We also describe how to use our
algorithm to improve the approximation ratio for the 3-level version of UFL.Comment: A journal versio
Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks
The characterization of the global maximum of energy efficiency (EE) problems
in wireless networks is a challenging problem due to the non-convex nature of
investigated problems in interference channels. The aim of this work is to
develop a new and general framework to achieve globally optimal solutions.
First, the hidden monotonic structure of the most common EE maximization
problems is exploited jointly with fractional programming theory to obtain
globally optimal solutions with exponential complexity in the number of network
links. To overcome this issue, we also propose a framework to compute
suboptimal power control strategies characterized by affordable complexity.
This is achieved by merging fractional programming and sequential optimization.
The proposed monotonic framework is used to shed light on the ultimate
performance of wireless networks in terms of EE and also to benchmark the
performance of the lower-complexity framework based on sequential programming.
Numerical evidence is provided to show that the sequential fractional
programming framework achieves global optimality in several practical
communication scenarios.Comment: Accepted for publication in the IEEE Transactions on Signal
Processin
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