69,649 research outputs found
Combining global optimization and boundary integral methods to robustly estimate subsurface velocity models
In this paper, we combine a fast wave equation solver using boundary integral methods with a global optimization method, namely Particle Swarm Optimization (PSO), to estimate an initial velocity model. Unlike finite difference methods that discretize the model space into pixels or voxels, our forward solver achieves significant computational savings by constraining the model space to a layered model with perturbations. The speed and reduced model space of the forward solve allows us to use global optimization methods that typically require numerous evaluations and few unknown variables. Our technique does not require an initial guess of a velocity model and is robust to local minima, unlike gradient descent frequently used in methods for both initial velocity model estimation and full waveform inversion. We apply our inversion algorithm to several synthetic data sets and demonstrate how prior information can be used to greatly improve the inversion
Sparsity-Cognizant Total Least-Squares for Perturbed Compressive Sampling
Solving linear regression problems based on the total least-squares (TLS)
criterion has well-documented merits in various applications, where
perturbations appear both in the data vector as well as in the regression
matrix. However, existing TLS approaches do not account for sparsity possibly
present in the unknown vector of regression coefficients. On the other hand,
sparsity is the key attribute exploited by modern compressive sampling and
variable selection approaches to linear regression, which include noise in the
data, but do not account for perturbations in the regression matrix. The
present paper fills this gap by formulating and solving TLS optimization
problems under sparsity constraints. Near-optimum and reduced-complexity
suboptimum sparse (S-) TLS algorithms are developed to address the perturbed
compressive sampling (and the related dictionary learning) challenge, when
there is a mismatch between the true and adopted bases over which the unknown
vector is sparse. The novel S-TLS schemes also allow for perturbations in the
regression matrix of the least-absolute selection and shrinkage selection
operator (Lasso), and endow TLS approaches with ability to cope with sparse,
under-determined "errors-in-variables" models. Interesting generalizations can
further exploit prior knowledge on the perturbations to obtain novel weighted
and structured S-TLS solvers. Analysis and simulations demonstrate the
practical impact of S-TLS in calibrating the mismatch effects of contemporary
grid-based approaches to cognitive radio sensing, and robust
direction-of-arrival estimation using antenna arrays.Comment: 30 pages, 10 figures, submitted to IEEE Transactions on Signal
Processin
A Kernel Perspective for Regularizing Deep Neural Networks
We propose a new point of view for regularizing deep neural networks by using
the norm of a reproducing kernel Hilbert space (RKHS). Even though this norm
cannot be computed, it admits upper and lower approximations leading to various
practical strategies. Specifically, this perspective (i) provides a common
umbrella for many existing regularization principles, including spectral norm
and gradient penalties, or adversarial training, (ii) leads to new effective
regularization penalties, and (iii) suggests hybrid strategies combining lower
and upper bounds to get better approximations of the RKHS norm. We
experimentally show this approach to be effective when learning on small
datasets, or to obtain adversarially robust models.Comment: ICM
Global stabilization of feedforward systems under perturbations in sampling schedule
For nonlinear systems that are known to be globally asymptotically
stabilizable, control over networks introduces a major challenge because of the
asynchrony in the transmission schedule. Maintaining global asymptotic
stabilization in sampled-data implementations with zero-order hold and with
perturbations in the sampling schedule is not achievable in general but we show
in this paper that it is achievable for the class of feedforward systems. We
develop sampled-data feedback stabilizers which are not approximations of
continuous-time designs but are discontinuous feedback laws that are
specifically developed for maintaining global asymptotic stabilizability under
any sequence of sampling periods that is uniformly bounded by a certain
"maximum allowable sampling period".Comment: 27 pages, 5 figures, submitted for possible publication to SIAM
Journal Control and Optimization. Second version with added remark
- …