28 research outputs found
Iterative Discretization of Optimization Problems Related to Superresolution
International audienceWe study an iterative discretization algorithm for solving optimization problems regularized by the total variation norm over the space M(Ω) of Radon measures on a bounded subset Ω of R d. Our main motivation to study this problem is the recovery of sparse atomic measures from linear measurements. Under reasonable regularity conditions, we arrive at a linear convergence rate guarantee
Optimization Dynamics of Equivariant and Augmented Neural Networks
We investigate the optimization of multilayer perceptrons on symmetric data.
We compare the strategy of constraining the architecture to be equivariant to
that of using augmentation. We show that, under natural assumptions on the loss
and non-linearities, the sets of equivariant stationary points are identical
for the two strategies, and that the set of equivariant layers is invariant
under the gradient flow for augmented models. Finally, we show that stationary
points may be unstable for augmented training although they are stable for the
equivariant models.Comment: v2: Revised manuscript. Mostly small edits, apart from new
experiments (see Appendix E
Reliable recovery of hierarchically sparse signals for Gaussian and Kronecker product measurements
We propose and analyze a solution to the problem of recovering a block sparse
signal with sparse blocks from linear measurements. Such problems naturally
emerge inter alia in the context of mobile communication, in order to meet the
scalability and low complexity requirements of massive antenna systems and
massive machine-type communication. We introduce a new variant of the Hard
Thresholding Pursuit (HTP) algorithm referred to as HiHTP. We provide both a
proof of convergence and a recovery guarantee for noisy Gaussian measurements
that exhibit an improved asymptotic scaling in terms of the sampling complexity
in comparison with the usual HTP algorithm. Furthermore, hierarchically sparse
signals and Kronecker product structured measurements naturally arise together
in a variety of applications. We establish the efficient reconstruction of
hierarchically sparse signals from Kronecker product measurements using the
HiHTP algorithm. Additionally, we provide analytical results that connect our
recovery conditions to generalized coherence measures. Again, our recovery
results exhibit substantial improvement in the asymptotic sampling complexity
scaling over the standard setting. Finally, we validate in numerical
experiments that for hierarchically sparse signals, HiHTP performs
significantly better compared to HTP.Comment: 11+4 pages, 5 figures. V3: Incomplete funding information corrected
and minor typos corrected. V4: Change of title and additional author Axel
Flinth. Included new results on Kronecker product measurements and relations
of HiRIP to hierarchical coherence measures. Improved presentation of general
hierarchically sparse signals and correction of minor typo