326 research outputs found
Random projections for linear programming
Random projections are random linear maps, sampled from appropriate
distributions, that approx- imately preserve certain geometrical invariants so
that the approximation improves as the dimension of the space grows. The
well-known Johnson-Lindenstrauss lemma states that there are random ma- trices
with surprisingly few rows that approximately preserve pairwise Euclidean
distances among a set of points. This is commonly used to speed up algorithms
based on Euclidean distances. We prove that these matrices also preserve other
quantities, such as the distance to a cone. We exploit this result to devise a
probabilistic algorithm to solve linear programs approximately. We show that
this algorithm can approximately solve very large randomly generated LP
instances. We also showcase its application to an error correction coding
problem.Comment: 26 pages, 1 figur
New error measures and methods for realizing protein graphs from distance data
The interval Distance Geometry Problem (iDGP) consists in finding a
realization in of a simple undirected graph with
nonnegative intervals assigned to the edges in such a way that, for each edge,
the Euclidean distance between the realization of the adjacent vertices is
within the edge interval bounds. In this paper, we focus on the application to
the conformation of proteins in space, which is a basic step in determining
protein function: given interval estimations of some of the inter-atomic
distances, find their shape. Among different families of methods for
accomplishing this task, we look at mathematical programming based methods,
which are well suited for dealing with intervals. The basic question we want to
answer is: what is the best such method for the problem? The most meaningful
error measure for evaluating solution quality is the coordinate root mean
square deviation. We first introduce a new error measure which addresses a
particular feature of protein backbones, i.e. many partial reflections also
yield acceptable backbones. We then present a set of new and existing quadratic
and semidefinite programming formulations of this problem, and a set of new and
existing methods for solving these formulations. Finally, we perform a
computational evaluation of all the feasible solverformulation combinations
according to new and existing error measures, finding that the best methodology
is a new heuristic method based on multiplicative weights updates
An Overview of Methods using Reduced-Ordered Transformation Matrices for Fault-Tolerant Control of 5-phase Machines with an Open Phase
This paper studies control strategies using modified transformation matrices when five-phase machines operate in oneopen-phase faults. The basic idea of these methods is to maintain the rotating field under asymmetrical conditions as the same as in healthy condition by determining new transformation matrices. The dimension of the new matrices is equal to the number of remaining healthy phases in post-fault conditions. There have been different ways to determine the new transformation matrices applied for different types of five-phase machines in recent decades. In this study, an overview and analyses on these methods will be presented. In addition, advantages and drawbacks of these control strategies are clarified by numerical results
Eliminations of Low-frequency Current Harmonics for Five-phase Open-end Winding Non-sinusoidal Machine Drives applying Neural Networks
This study aims at eliminating unwanted harmonics in current control of a five-phase non-sinusoidal permanent magnet synchronous machine (PMSM) in an open-end winding configuration. The machine is supplied by two voltage source inverters (VSIs) using a single DC-bus voltage. High-frequency harmonics, caused by the zero-sequence current with the inverter switching frequency, have been significantly reduced by using a proper pulse width modulation (PWM) strategy. Meanwhile, low-frequency current harmonics are generated by unwanted harmonics of the back electromotive force (back-EMF) and by the inverter nonlinearity. In this study, the low-frequency current harmonics are nullified by simple adaptive linear neural networks (ADALINEs) in rotor reference frames combined with the back-EMF compensation. As a result, the quality of current control is improved. The effectiveness of the proposed strategies is verified by numerical resultsThis work has been achieved within the framework of CE2I project. CE2I is co-financed by European Union with the financial support of European Regional Development Fund (ERDF), French State and the French Region of Hauts-de-France
- …