A tutorial on adaptive MCMC
C Andrieu, J Thoms - Statistics and computing, 2008 - Springer
We review adaptive Markov chain Monte Carlo algorithms (MCMC) as a mean to optimise
their performance. Using simple toy examples we review their theoretical underpinnings …
their performance. Using simple toy examples we review their theoretical underpinnings …
Simulation optimization: A review and exploration in the new era of cloud computing and big data
Recent advances in simulation optimization research and explosive growth in computing
power have made it possible to optimize complex stochastic systems that are otherwise …
power have made it possible to optimize complex stochastic systems that are otherwise …
On the use of deep learning for computational imaging
Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …
and machine learning have followed parallel tracks and, during the last two decades …
Minimizing finite sums with the stochastic average gradient
We analyze the stochastic average gradient (SAG) method for optimizing the sum of a finite
number of smooth convex functions. Like stochastic gradient (SG) methods, the SAG …
number of smooth convex functions. Like stochastic gradient (SG) methods, the SAG …
Painless stochastic gradient: Interpolation, line-search, and convergence rates
Recent works have shown that stochastic gradient descent (SGD) achieves the fast
convergence rates of full-batch gradient descent for over-parameterized models satisfying …
convergence rates of full-batch gradient descent for over-parameterized models satisfying …
A stochastic gradient method with an exponential convergence _rate for finite training sets
We propose a new stochastic gradient method for optimizing the sum of a finite set of smooth
functions, where the sum is strongly convex. While standard stochastic gradient methods …
functions, where the sum is strongly convex. While standard stochastic gradient methods …
[图书][B] Random iterative models
M Duflo - 2013 - books.google.com
Be they random or non-random, iterative methods have progressively gained sway with the
development of computer science and automatic control theory. Thus, being easy to …
development of computer science and automatic control theory. Thus, being easy to …
Learning control for motion coordination in wafer scanners: toward gain adaptation
F Song, Y Liu, D Shen, L Li, J Tan - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Accurate pattern transfer in wafer scanners necessitates the wafer stage and the reticle
stage executing a coordinated motion with the synchronization error in terms of nanometers …
stage executing a coordinated motion with the synchronization error in terms of nanometers …
A review of simulation optimization techniques
S Andradóttir - 1998 winter simulation conference. Proceedings …, 1998 - ieeexplore.ieee.org
We present a review of methods for optimizing stochastic systems using simulation. The
focus is on gradient based techniques for optimization with respect to continuous decision …
focus is on gradient based techniques for optimization with respect to continuous decision …
[PDF][PDF] Simulation optimization
S Andradóttir - Handbook of simulation, 1998 - academia.edu
In this chapter we consider how simulation can be used to design a system to yield optimal
expected performance. More specifically, we assume that the performance of the system of …
expected performance. More specifically, we assume that the performance of the system of …