41,772 research outputs found
Online Learning of a Memory for Learning Rates
The promise of learning to learn for robotics rests on the hope that by
extracting some information about the learning process itself we can speed up
subsequent similar learning tasks. Here, we introduce a computationally
efficient online meta-learning algorithm that builds and optimizes a memory
model of the optimal learning rate landscape from previously observed gradient
behaviors. While performing task specific optimization, this memory of learning
rates predicts how to scale currently observed gradients. After applying the
gradient scaling our meta-learner updates its internal memory based on the
observed effect its prediction had. Our meta-learner can be combined with any
gradient-based optimizer, learns on the fly and can be transferred to new
optimization tasks. In our evaluations we show that our meta-learning algorithm
speeds up learning of MNIST classification and a variety of learning control
tasks, either in batch or online learning settings.Comment: accepted to ICRA 2018, code available:
https://github.com/fmeier/online-meta-learning ; video pitch available:
https://youtu.be/9PzQ25FPPO
An Efficient Algorithm for Automatic Structure Optimization in X-ray Standing-Wave Experiments
X-ray standing-wave photoemission experiments involving multilayered samples
are emerging as unique probes of the buried interfaces that are ubiquitous in
current device and materials research. Such data require for their analysis a
structure optimization process comparing experiment to theory that is not
straightforward. In this work, we present a new computer program for optimizing
the analysis of standing-wave data, called SWOPT, that automates this
trial-and-error optimization process. The program includes an algorithm that
has been developed for computationally expensive problems: so-called black-box
simulation optimizations. It also includes a more efficient version of the Yang
X-ray Optics Program (YXRO) [Yang, S.-H., Gray, A.X., Kaiser, A.M., Mun, B.S.,
Sell, B.C., Kortright, J.B., Fadley, C.S., J. Appl. Phys. 113, 1 (2013)] which
is about an order of magnitude faster than the original version. Human
interaction is not required during optimization. We tested our optimization
algorithm on real and hypothetical problems and show that it finds better
solutions significantly faster than a random search approach. The total
optimization time ranges, depending on the sample structure, from minutes to a
few hours on a modern laptop computer, and can be up to 100x faster than a
corresponding manual optimization. These speeds make the SWOPT program a
valuable tool for realtime analyses of data during synchrotron experiments
To Index or Not to Index: Optimizing Exact Maximum Inner Product Search
Exact Maximum Inner Product Search (MIPS) is an important task that is widely
pertinent to recommender systems and high-dimensional similarity search. The
brute-force approach to solving exact MIPS is computationally expensive, thus
spurring recent development of novel indexes and pruning techniques for this
task. In this paper, we show that a hardware-efficient brute-force approach,
blocked matrix multiply (BMM), can outperform the state-of-the-art MIPS solvers
by over an order of magnitude, for some -- but not all -- inputs.
In this paper, we also present a novel MIPS solution, MAXIMUS, that takes
advantage of hardware efficiency and pruning of the search space. Like BMM,
MAXIMUS is faster than other solvers by up to an order of magnitude, but again
only for some inputs. Since no single solution offers the best runtime
performance for all inputs, we introduce a new data-dependent optimizer,
OPTIMUS, that selects online with minimal overhead the best MIPS solver for a
given input. Together, OPTIMUS and MAXIMUS outperform state-of-the-art MIPS
solvers by 3.2 on average, and up to 10.9, on widely studied
MIPS datasets.Comment: 12 pages, 8 figures, 2 table
Distributed multilevel optimization for complex structures
Optimization problems concerning complex structures with many design variables may entail an unacceptable computational cost. This problem can be reduced considerably with a multilevel approach: A structure consisting of several components is optimized as a whole (global) as well as on the component level. In this paper, an optimization method is discussed with applications in the assessment of the impact of new design considerations in the development of a structure. A strategy based on fully stressed design is applied for optimization problems in linear statics. A global model is used to calculate the interactions (e.g., loads) for each of the components. These components are then optimized using the prescribed interactions, followed by a new global calculation to update the interactions. Mixed discrete and continuous design variables as well as different design configurations are possible. An application of this strategy is presented in the form of the full optimization of a vertical tail plane center box of a generic large passenger aircraft. In linear dynamics, the parametrization of the component interactions is problematic due to the frequency dependence. Hence, a modified method is presented in which the speed of component mode synthesis is used to avoid this parametrization. This method is applied to a simple test case that originates from noise control. \u
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