41,205 research outputs found
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
On-line planning and scheduling: an application to controlling modular printers
We present a case study of artificial intelligence techniques applied to the control of production printing equipment. Like many other real-world applications, this complex domain requires high-speed autonomous decision-making and robust continual operation. To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning. Our system handles execution failures and multi-objective preferences. At its heart is an on-line algorithm that combines techniques from state-space planning and partial-order scheduling. We suggest that this general architecture may prove useful in other applications as more intelligent systems operate in continual, on-line settings. Our system has been used to drive several commercial prototypes and has enabled a new product architecture for our industrial partner. When compared with state-of-the-art off-line planners, our system is hundreds of times faster and often finds better plans. Our experience demonstrates that domain-independent AI planning based on heuristic search can flexibly handle time, resources, replanning, and multiple objectives in a high-speed practical application without requiring hand-coded control knowledge
Program representation size in an intermediate language with intersection and union types
The CIL compiler for core Standard ML compiles whole programs using a novel typed intermediate language (TIL) with intersection and union types and flow labels on both terms and types. The CIL term representation duplicates portions of the program where intersection types are introduced and union types are eliminated. This duplication makes it easier to represent type information and to introduce customized data representations. However, duplication incurs compile-time space costs that are potentially much greater than are incurred in TILs employing type-level abstraction or quantification. In this paper, we present empirical data on the compile-time space costs of using CIL as an intermediate language. The data shows that these costs can be made tractable by using sufficiently fine-grained flow analyses together with standard hash-consing techniques. The data also suggests that non-duplicating formulations of intersection (and union) types would not achieve significantly better space complexity.National Science Foundation (CCR-9417382, CISE/CCR ESS 9806747); Sun grant (EDUD-7826-990410-US); Faculty Fellowship of the Carroll School of Management, Boston College; U.K. Engineering and Physical Sciences Research Council (GR/L 36963, GR/L 15685
Building Efficient Query Engines in a High-Level Language
Abstraction without regret refers to the vision of using high-level
programming languages for systems development without experiencing a negative
impact on performance. A database system designed according to this vision
offers both increased productivity and high performance, instead of sacrificing
the former for the latter as is the case with existing, monolithic
implementations that are hard to maintain and extend. In this article, we
realize this vision in the domain of analytical query processing. We present
LegoBase, a query engine written in the high-level language Scala. The key
technique to regain efficiency is to apply generative programming: LegoBase
performs source-to-source compilation and optimizes the entire query engine by
converting the high-level Scala code to specialized, low-level C code. We show
how generative programming allows to easily implement a wide spectrum of
optimizations, such as introducing data partitioning or switching from a row to
a column data layout, which are difficult to achieve with existing low-level
query compilers that handle only queries. We demonstrate that sufficiently
powerful abstractions are essential for dealing with the complexity of the
optimization effort, shielding developers from compiler internals and
decoupling individual optimizations from each other. We evaluate our approach
with the TPC-H benchmark and show that: (a) With all optimizations enabled,
LegoBase significantly outperforms a commercial database and an existing query
compiler. (b) Programmers need to provide just a few hundred lines of
high-level code for implementing the optimizations, instead of complicated
low-level code that is required by existing query compilation approaches. (c)
The compilation overhead is low compared to the overall execution time, thus
making our approach usable in practice for compiling query engines
Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control
Constrained optimization of high-dimensional numerical problems plays an
important role in many scientific and industrial applications. Function
evaluations in many industrial applications are severely limited and no
analytical information about objective function and constraint functions is
available. For such expensive black-box optimization tasks, the constraint
optimization algorithm COBRA was proposed, making use of RBF surrogate modeling
for both the objective and the constraint functions. COBRA has shown remarkable
success in solving reliably complex benchmark problems in less than 500
function evaluations. Unfortunately, COBRA requires careful adjustment of
parameters in order to do so.
In this work we present a new self-adjusting algorithm SACOBRA, which is
based on COBRA and capable to achieve high-quality results with very few
function evaluations and no parameter tuning. It is shown with the help of
performance profiles on a set of benchmark problems (G-problems, MOPTA08) that
SACOBRA consistently outperforms any COBRA algorithm with fixed parameter
setting. We analyze the importance of the several new elements in SACOBRA and
find that each element of SACOBRA plays a role to boost up the overall
optimization performance. We discuss the reasons behind and get in this way a
better understanding of high-quality RBF surrogate modeling
Sparking Social Innovations
{Excerpt} Necessity is the mother of invention. The demand for good ideas, put into practice, that meet pressing unmet needs and improve people’s lives is growing on a par with the agenda of the 21st century. In a shrinking world, social innovation at requisite institutional levels can do much to foster smart, sustainable globalization.
In consequence of successive scientific revolutions, mankind has changed its conditions and capacities with increasing speed. Globalization is a given: today, mankind’s activities are affecting the entire planet—and thereby mankind itself—for good and ill.
A select list of the worldwide challenges we face includes alleviating poverty; mitigating and adapting to climate change; ending abuse of natural resources and the environment; cleaning up environmental pollution; dealing with natural disasters; countering medical challenges, e.g., pandemics; encouraging disarmament; coping with security threats; accommodating nonstate power; handling failed states; tapping capacity for social action; allaying frustration among minorities; confronting violence; identifying global rights; building a global rule of law; evolving regulatory and institutional frameworks to contain global financial and economic crises; optimizing international trade; managing mass migrations; employing human resources better; and optimizing knowledge
Models and Techniques for Hotel Revenue Management Using a Roling Horizon
AbstractThis paper studies decision rules for accepting reservations for stays in a hotel based on deterministic and stochastic mathematical programming techniques. Booking control strategies are constructed that include ideas for nesting, booking limits and bid prices. We allow for multiple day stays. Instead of optimizing a decision period consisting of a fixed set of target booking days, we simultaneously optimize the complete range of target booking dates that are open for booking at the moment of optimization. This yields a rolling horizon of overlapping decision periods, which will conveniently capture the effects of overlapping stays.mathematical programming;Revenue Management;yield management
Inferring Types to Eliminate Ownership Checks in an Intentional JavaScript Compiler
Concurrent programs are notoriously difficult to develop due to the non-deterministic nature of thread scheduling. It is desirable to have a programming language to make such development easier. Tscript comprises such a system. Tscript is an extension of JavaScript that provides multithreading support along with intent specification. These intents allow a programmer to specify how parts of the program interact in a multithreaded context. However, enforcing intents requires run-time memory checks which can be inefficient. This thesis implements an optimization in the Tscript compiler that seeks to improve this inefficiency through static analysis. Our approach utilizes both type inference and dataflow analysis to eliminate unnecessary run-time checks
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