6,197 research outputs found
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Algorithm Engineering in Robust Optimization
Robust optimization is a young and emerging field of research having received
a considerable increase of interest over the last decade. In this paper, we
argue that the the algorithm engineering methodology fits very well to the
field of robust optimization and yields a rewarding new perspective on both the
current state of research and open research directions.
To this end we go through the algorithm engineering cycle of design and
analysis of concepts, development and implementation of algorithms, and
theoretical and experimental evaluation. We show that many ideas of algorithm
engineering have already been applied in publications on robust optimization.
Most work on robust optimization is devoted to analysis of the concepts and the
development of algorithms, some papers deal with the evaluation of a particular
concept in case studies, and work on comparison of concepts just starts. What
is still a drawback in many papers on robustness is the missing link to include
the results of the experiments again in the design
Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic
Rapidly-exploring random trees (RRTs) are popular in motion planning because
they find solutions efficiently to single-query problems. Optimal RRTs (RRT*s)
extend RRTs to the problem of finding the optimal solution, but in doing so
asymptotically find the optimal path from the initial state to every state in
the planning domain. This behaviour is not only inefficient but also
inconsistent with their single-query nature.
For problems seeking to minimize path length, the subset of states that can
improve a solution can be described by a prolate hyperspheroid. We show that
unless this subset is sampled directly, the probability of improving a solution
becomes arbitrarily small in large worlds or high state dimensions. In this
paper, we present an exact method to focus the search by directly sampling this
subset.
The advantages of the presented sampling technique are demonstrated with a
new algorithm, Informed RRT*. This method retains the same probabilistic
guarantees on completeness and optimality as RRT* while improving the
convergence rate and final solution quality. We present the algorithm as a
simple modification to RRT* that could be further extended by more advanced
path-planning algorithms. We show experimentally that it outperforms RRT* in
rate of convergence, final solution cost, and ability to find difficult
passages while demonstrating less dependence on the state dimension and range
of the planning problem.Comment: 8 pages, 11 figures. Videos available at
https://www.youtube.com/watch?v=d7dX5MvDYTc and
https://www.youtube.com/watch?v=nsl-5MZfwu
Mapping constrained optimization problems to quantum annealing with application to fault diagnosis
Current quantum annealing (QA) hardware suffers from practical limitations
such as finite temperature, sparse connectivity, small qubit numbers, and
control error. We propose new algorithms for mapping boolean constraint
satisfaction problems (CSPs) onto QA hardware mitigating these limitations. In
particular we develop a new embedding algorithm for mapping a CSP onto a
hardware Ising model with a fixed sparse set of interactions, and propose two
new decomposition algorithms for solving problems too large to map directly
into hardware.
The mapping technique is locally-structured, as hardware compatible Ising
models are generated for each problem constraint, and variables appearing in
different constraints are chained together using ferromagnetic couplings. In
contrast, global embedding techniques generate a hardware independent Ising
model for all the constraints, and then use a minor-embedding algorithm to
generate a hardware compatible Ising model. We give an example of a class of
CSPs for which the scaling performance of D-Wave's QA hardware using the local
mapping technique is significantly better than global embedding.
We validate the approach by applying D-Wave's hardware to circuit-based
fault-diagnosis. For circuits that embed directly, we find that the hardware is
typically able to find all solutions from a min-fault diagnosis set of size N
using 1000N samples, using an annealing rate that is 25 times faster than a
leading SAT-based sampling method. Further, we apply decomposition algorithms
to find min-cardinality faults for circuits that are up to 5 times larger than
can be solved directly on current hardware.Comment: 22 pages, 4 figure
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