Related papers: Stochastic parallel gradient descent based adaptiv…

Stochastic Gradient Descent (SGD) is a known stochastic iterative method popular for large-scale convex optimization problems due to its simple implementation and scalability. Some objectives, such as those found in complex-valued neural…

The vanilla fractional order gradient descent may oscillatively converge to a region around the global minimum instead of converging to the exact minimum point, or even diverge, in the case where the objective function is strongly convex.…

In this paper, we propose a novel kernel stochastic gradient descent (SGD) algorithm for large-scale supervised learning with general losses. Compared to traditional kernel SGD, our algorithm improves efficiency and scalability through an…

Stochastic Gradient Decent (SGD) is one of the core techniques behind the success of deep neural networks. The gradient provides information on the direction in which a function has the steepest rate of change. The main problem with basic…

The stochastic gradient descent (SGD) optimization algorithm plays a central role in a series of machine learning applications. The scientific literature provides a vast amount of upper error bounds for the SGD method. Much less attention…

Diffuse speckle contrast analysis (DSCA), also called speckle contrast optical spectroscopy(SCOS), has emerged as a groundbreaking optical imaging technique for tracking dynamic biological processes, including blood flow and tissue…

In this paper, we propose a scalable algorithm for spectral embedding. The latter is a standard tool for graph clustering. However, its computational bottleneck is the eigendecomposition of the graph Laplacian matrix, which prevents its…

The performance of an adaptive optics (AO) system on a 100m diameter ground based telescope working in the visible range of the spectrum is computed using an analytical approach. The target Strehl ratio of 60% is achieved at 0.5um with a…

In this dissertation we propose alternative analysis of distributed stochastic gradient descent (SGD) algorithms that rely on spectral properties of the data covariance. As a consequence we can relate questions pertaining to speedups and…

The Phase-Induced Amplitude Apodization (PIAA) coronagraph is a high performance coronagraph concept able to work at small angular separation with little loss in throughput. We present results obtained with a laboratory PIAA system…

In ground-based high-contrast instruments, non-common path aberrations (NCPAs) limit detection performance, as they are unseen by the adaptive optics (AO) wavefront sensor but impact the astrophysical image, creating quasi-static speckles.…

In direct imaging at high contrast, the bright glare produced by the host star makes the detection and the characterization of sub-stellar companions particularly challenging. In spite of the use of an extreme adaptive optics system…

Dual averaging and gradient descent with their stochastic variants stand as the two canonical recipe books for first-order optimization: Every modern variant can be viewed as a descendant of one or the other. In the convex regime, these…

Described here is the design and commissioning of a coronagraph facility for the 4.2 metre William Herschel Telescope (WHT) and its Nasmyth Adaptive Optics system for Multi-purpose Instrumentation (NAOMI). The use of the NAOMI system gives…

The next generation of Adaptive Optics (AO) systems on large telescopes will require immense computation performance and memory bandwidth, both of which are challenging with the technology available today. The objective of this work is to…

Adaptive optic (AO) systems delivering high levels of wavefront correction are now common at observatories. One of the main limitations to image quality after wavefront correction comes from atmospheric refraction. An Atmospheric dispersion…

One of the most common methods to train machine learning algorithms today is the stochastic gradient descent (SGD). In a distributed setting, SGD-based algorithms have been shown to converge theoretically under specific circumstances. A…

We present early laboratory simulations and extensive on-sky tests validating of the performance of a shaped pupil coronagraph (SPC) behind an extreme-AO corrected beam of the Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) system. In…

Most current astronomical adaptive optics (AO) systems rely on the availability of a bright star to measure the distortion of the incoming wavefront. Replacing the guide star with an artificial laser beacon alleviates this dependency on…

As the size of models and datasets grows, it has become increasingly common to train models in parallel. However, existing distributed stochastic gradient descent (SGD) algorithms suffer from insufficient utilization of computational…