40,882 research outputs found
Set-based design of mechanical systems with design robustness integrated
This paper presents a method for parameter design of mechanical products based on a set-based approach. Set-based concurrent engineering emphasises on designing in a multi-stakeholder environment with concurrent involvement of the stakeholders in the design process. It also encourages flexibility in design through communication in terms of ranges instead of fixed point values and subsequent alternative solutions resulting from intersection of these ranges. These alternative solutions can then be refined and selected according to the designers’ preferences and clients’ needs. This paper presents a model and tools for integrated flexible design that take into account the manufacturing variations as well as the design objectives for finding inherently robust solutions using QCSP transformation through interval analysis. In order to demonstrate the approach, an example of design of rigid flange coupling with a variable number of bolts and a choice of bolts from ISO M standard has been resolved and demonstrated
Bayesian Optimization for Probabilistic Programs
We present the first general purpose framework for marginal maximum a
posteriori estimation of probabilistic program variables. By using a series of
code transformations, the evidence of any probabilistic program, and therefore
of any graphical model, can be optimized with respect to an arbitrary subset of
its sampled variables. To carry out this optimization, we develop the first
Bayesian optimization package to directly exploit the source code of its
target, leading to innovations in problem-independent hyperpriors, unbounded
optimization, and implicit constraint satisfaction; delivering significant
performance improvements over prominent existing packages. We present
applications of our method to a number of tasks including engineering design
and parameter optimization
A comprehensive literature classification of simulation optimisation methods
Simulation Optimization (SO) provides a structured approach to the system design and configuration when analytical expressions for input/output relationships are unavailable. Several excellent surveys have been written on this topic. Each survey concentrates on only few classification criteria. This paper presents a literature survey with all classification criteria on techniques for SO according to the problem of characteristics such as shape of the response surface (global as compared to local optimization), objective functions (single or multiple objectives) and parameter spaces (discrete or continuous parameters). The survey focuses specifically on the SO problem that involves single per-formance measureSimulation Optimization, classification methods, literature survey
Sequential Design for Ranking Response Surfaces
We propose and analyze sequential design methods for the problem of ranking
several response surfaces. Namely, given response surfaces over a
continuous input space , the aim is to efficiently find the index of
the minimal response across the entire . The response surfaces are not
known and have to be noisily sampled one-at-a-time. This setting is motivated
by stochastic control applications and requires joint experimental design both
in space and response-index dimensions. To generate sequential design
heuristics we investigate stepwise uncertainty reduction approaches, as well as
sampling based on posterior classification complexity. We also make connections
between our continuous-input formulation and the discrete framework of pure
regret in multi-armed bandits. To model the response surfaces we utilize
kriging surrogates. Several numerical examples using both synthetic data and an
epidemics control problem are provided to illustrate our approach and the
efficacy of respective adaptive designs.Comment: 26 pages, 7 figures (updated several sections and figures
An Efficient Bandit Algorithm for Realtime Multivariate Optimization
Optimization is commonly employed to determine the content of web pages, such
as to maximize conversions on landing pages or click-through rates on search
engine result pages. Often the layout of these pages can be decoupled into
several separate decisions. For example, the composition of a landing page may
involve deciding which image to show, which wording to use, what color
background to display, etc. Such optimization is a combinatorial problem over
an exponentially large decision space. Randomized experiments do not scale well
to this setting, and therefore, in practice, one is typically limited to
optimizing a single aspect of a web page at a time. This represents a missed
opportunity in both the speed of experimentation and the exploitation of
possible interactions between layout decisions.
Here we focus on multivariate optimization of interactive web pages. We
formulate an approach where the possible interactions between different
components of the page are modeled explicitly. We apply bandit methodology to
explore the layout space efficiently and use hill-climbing to select optimal
content in realtime. Our algorithm also extends to contextualization and
personalization of layout selection. Simulation results show the suitability of
our approach to large decision spaces with strong interactions between content.
We further apply our algorithm to optimize a message that promotes adoption of
an Amazon service. After only a single week of online optimization, we saw a
21% conversion increase compared to the median layout. Our technique is
currently being deployed to optimize content across several locations at
Amazon.com.Comment: KDD'17 Audience Appreciation Awar
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