18 research outputs found
An inexact SQP Newton method for convex SC1 minimization problems
Master'sMASTER OF SCIENC
Worst-Case Linear Discriminant Analysis as Scalable Semidefinite Feasibility Problems
In this paper, we propose an efficient semidefinite programming (SDP)
approach to worst-case linear discriminant analysis (WLDA). Compared with the
traditional LDA, WLDA considers the dimensionality reduction problem from the
worst-case viewpoint, which is in general more robust for classification.
However, the original problem of WLDA is non-convex and difficult to optimize.
In this paper, we reformulate the optimization problem of WLDA into a sequence
of semidefinite feasibility problems. To efficiently solve the semidefinite
feasibility problems, we design a new scalable optimization method with
quasi-Newton methods and eigen-decomposition being the core components. The
proposed method is orders of magnitude faster than standard interior-point
based SDP solvers.
Experiments on a variety of classification problems demonstrate that our
approach achieves better performance than standard LDA. Our method is also much
faster and more scalable than standard interior-point SDP solvers based WLDA.
The computational complexity for an SDP with constraints and matrices of
size by is roughly reduced from to
( in our case).Comment: 14 page
Algorithms for Large Scale Nuclear Norm Minimization and Convex Quadratic Semidefinite Programming Problems
Ph.DDOCTOR OF PHILOSOPH
L1 data fitting for robust reconstruction in magnetic particle imaging: quantitative evaluation on Open MPI dataset
Magnetic particle imaging is an emerging quantitative imaging modality,
exploiting the unique nonlinear magnetization phenomenon of superparamagnetic
iron oxide nanoparticles for recovering the concentration. Traditionally the
reconstruction is formulated into a penalized least-squares problem with
nonnegativity constraint, and then solved using a variant of Kaczmarz method
which is often stopped early after a small number of iterations. Besides the
phantom signal, measurements additionally include a background signal and a
noise signal. In order to obtain good reconstructions, a preprocessing step of
frequency selection to remove the deleterious influences of the noise is often
adopted. In this work, we propose a complementary pure variational approach to
noise treatment, by viewing highly noisy measurements as outliers, and
employing the l1 data fitting, one popular approach from robust statistics.
When compared with the standard approach, it is easy to implement with a
comparable computational complexity. Experiments with a public domain dataset,
i.e., Open MPI dataset, show that it can give accurate reconstructions, and is
less prone to noisy measurements, which is illustrated by quantitative (PSNR /
SSIM) and qualitative comparisons with the Kaczmarz method. We also investigate
the performance of the Kaczmarz method for small iteration numbers
quantitatively
A Semismooth Newton-CG Augmented Lagrangian Method for Large Scale Linear and Convex Quadratic SDPS
Ph.DDOCTOR OF PHILOSOPH
Inexact Interior-Point Methods for Large Scale Linear and Convex Quadratic Semidefinite Programming
Ph.DDOCTOR OF PHILOSOPH