146 research outputs found
Parallel eigenvalue reordering in real Schur forms
A parallel algorithm for reordering the eigenvalues in the real Schur form of a matrix is presented and discussed. Our novel approach adopts computational windows and delays multiple outside-window updates until each window has been completely reordered locally. By using multiple concurrent windows the parallel algorithm has a high level of concurrency, and most work is level 3 BLAS operations. The presented algorithm is also extended to the generalized real Schur form. Experimental results for ScaLAPACK-style Fortran 77 implementations on a Linux cluster confirm the efficiency and scalability of our algorithms in terms of more than 16 times of parallel speedup using 64 processors for large-scale problems. Even on a single processor our implementation is demonstrated to perform significantly better compared with the state-of-the-art serial implementation. Copyright (C) 2009 John Wiley & Sons, Ltd
How and why to solve the operator equation AX-XB= Y
The entities A, B, X, Y in the title are operators, by which we mean either linear transformations on a finite-dimensional vector space (matrices) or bounded (= continuous) linear transformations on a Banach space. (All scalars will be complex numbers.) The definitions and statements below are valid in both the finite-dimensional and the infinite-dimensional cases, unless the contrary is stated
New optimization methods in predictive control
This thesis is mainly concerned with the efficient solution of a linear discrete-time
finite horizon optimal control problem (FHOCP) with quadratic cost and linear constraints
on the states and inputs. In predictive control, such a FHOCP needs to be
solved online at each sampling instant. In order to solve such a FHOCP, it is necessary
to solve a quadratic programming (QP) problem. Interior point methods (IPMs) have
proven to be an efficient way of solving quadratic programming problems. A linear system
of equations needs to be solved in each iteration of an IPM. The ill-conditioning
of this linear system in the later iterations of the IPM prevents the use of an iterative
method in solving the linear system due to a very slow rate of convergence; in some cases
the solution never reaches the desired accuracy. A new well-conditioned IPM, which increases
the rate of convergence of the iterative method is proposed. The computational
advantage is obtained by the use of an inexact Newton method along with the use of
novel preconditioners.
A new warm-start strategy is also presented to solve a QP with an interior-point
method whose data is slightly perturbed from the previous QP. The effectiveness of
this warm-start strategy is demonstrated on a number of available online benchmark
problems. Numerical results indicate that the proposed technique depends upon the
size of perturbation and it leads to a reduction of 30-74% in floating point operations
compared to a cold-start interior point method.
Following the main theme of this thesis, which is to improve the computational efficiency
of an algorithm, an efficient algorithm for solving the coupled Sylvester equation
that arises in converting a system of linear differential-algebraic equations (DAEs) to
ordinary differential equations is also presented. A significant computational advantage
is obtained by exploiting the structure of the involved matrices. The proposed algorithm
removes the need to solve a standard Sylvester equation or to invert a matrix. The
improved performance of this new method over existing techniques is demonstrated by
comparing the number of floating-point operations and via numerical examples
Information Geometry
This Special Issue of the journal Entropy, titled “Information Geometry I”, contains a collection of 17 papers concerning the foundations and applications of information geometry. Based on a geometrical interpretation of probability, information geometry has become a rich mathematical field employing the methods of differential geometry. It has numerous applications to data science, physics, and neuroscience. Presenting original research, yet written in an accessible, tutorial style, this collection of papers will be useful for scientists who are new to the field, while providing an excellent reference for the more experienced researcher. Several papers are written by authorities in the field, and topics cover the foundations of information geometry, as well as applications to statistics, Bayesian inference, machine learning, complex systems, physics, and neuroscience
Learning Algebraic Varieties from Samples
We seek to determine a real algebraic variety from a fixed finite subset of
points. Existing methods are studied and new methods are developed. Our focus
lies on aspects of topology and algebraic geometry, such as dimension and
defining polynomials. All algorithms are tested on a range of datasets and made
available in a Julia package
Control of fluid flows and other systems governed by partial differential-algebraic equations
The motion of fluids, such as air or water, is central to many engineering systems of significant
economic and environmental importance. Examples range from air/fuel mixing in combustion engines
to turbulence induced noise and fatigue on aircraft. Recent advances in novel sensor/actuator
technologies have raised the intriguing prospect of actively sensing and manipulating the motion
of the fluid within these systems, making them ripe for feedback control, provided a suitable control
model exists. Unfortunately, the models for many of these systems are described by nonlinear,
partial differential-algebraic equations for which few, if any, controller synthesis techniques exist.
In stark contrast, the majority of established control theory assumes plant models of finite (and
typically small) state dimension, expressed as a linear system of ordinary differential equations.
Therefore, this thesis explores the problem of how to apply the mainstream tools of control theory
to the class of systems described by partial differential-algebraic equations, that are either linear,
or for which a linear approximation is valid.
The problems of control system design for infinite-dimensional and algebraically constrained
systems are treated separately in this thesis. With respect to the former, a new method is presented
that enables the computation of a bound on the n-gap between a discretisation of a spatially distributed
plant, and the plant itself, by exploiting the convergence rate of the v-gap metric between
low-order models of successively finer spatial resolution. This bound informs the design, on loworder
models, of H[infinity] loop-shaping controllers that are guaranteed to robustly stabilise the actual
plant. An example is presented on a one-dimensional heat equation.
Controller/estimator synthesis is then discussed for finite-dimensional systems containing algebraic,
as well as differential equations. In the case of fluid flows, algebraic constraints typically
arise from incompressibility and the application of boundary conditions. A numerical algorithm is
presented, suitable for the semi-discrete linearised Navier-Stokes equations, that decouples the differential
and algebraic parts of the system, enabling application of standard control theory without
the need for velocity-vorticity type methods. This algorithm is demonstrated firstly on a simple
electrical circuit, and secondly on the highly non-trivial problem of flow-field estimation in the
transient growth region of a flat-plate boundary layer, using only wall shear measurements.
These separate strands are woven together in the penultimate chapter, where a transient energy
controller is designed for a channel-flow system, using wall mounted sensors and actuators
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