35,768 research outputs found
A Domain Specific Approach to High Performance Heterogeneous Computing
Users of heterogeneous computing systems face two problems: firstly, in
understanding the trade-off relationships between the observable
characteristics of their applications, such as latency and quality of the
result, and secondly, how to exploit knowledge of these characteristics to
allocate work to distributed computing platforms efficiently. A domain specific
approach addresses both of these problems. By considering a subset of
operations or functions, models of the observable characteristics or domain
metrics may be formulated in advance, and populated at run-time for task
instances. These metric models can then be used to express the allocation of
work as a constrained integer program, which can be solved using heuristics,
machine learning or Mixed Integer Linear Programming (MILP) frameworks. These
claims are illustrated using the example domain of derivatives pricing in
computational finance, with the domain metrics of workload latency or makespan
and pricing accuracy. For a large, varied workload of 128 Black-Scholes and
Heston model-based option pricing tasks, running upon a diverse array of 16
Multicore CPUs, GPUs and FPGAs platforms, predictions made by models of both
the makespan and accuracy are generally within 10% of the run-time performance.
When these models are used as inputs to machine learning and MILP-based
workload allocation approaches, a latency improvement of up to 24 and 270 times
over the heuristic approach is seen.Comment: 14 pages, preprint draft, minor revisio
Sum-of-Squares approach to feedback control of laminar wake flows
A novel nonlinear feedback control design methodology for incompressible
fluid flows aiming at the optimisation of long-time averages of flow quantities
is presented. It applies to reduced-order finite-dimensional models of fluid
flows, expressed as a set of first-order nonlinear ordinary differential
equations with the right-hand side being a polynomial function in the state
variables and in the controls. The key idea, first discussed in Chernyshenko et
al. 2014, Philos. T. Roy. Soc. 372(2020), is that the difficulties of treating
and optimising long-time averages of a cost are relaxed by using the
upper/lower bounds of such averages as the objective function. In this setting,
control design reduces to finding a feedback controller that optimises the
bound, subject to a polynomial inequality constraint involving the cost
function, the nonlinear system, the controller itself and a tunable polynomial
function. A numerically tractable approach to the solution of such optimisation
problems, based on Sum-of-Squares techniques and semidefinite programming, is
proposed.
To showcase the methodology, the mitigation of the fluctuation kinetic energy
in the unsteady wake behind a circular cylinder in the laminar regime at
Re=100, via controlled angular motions of the surface, is numerically
investigated. A compact reduced-order model that resolves the long-term
behaviour of the fluid flow and the effects of actuation, is derived using
Proper Orthogonal Decomposition and Galerkin projection. In a full-information
setting, feedback controllers are then designed to reduce the long-time average
of the kinetic energy associated with the limit cycle. These controllers are
then implemented in direct numerical simulations of the actuated flow. Control
performance, energy efficiency, and physical control mechanisms identified are
analysed. Key elements, implications and future work are discussed
Branch-and-lift algorithm for deterministic global optimization in nonlinear optimal control
This paper presents a branch-and-lift algorithm for solving optimal control problems with smooth nonlinear dynamics and potentially nonconvex objective and constraint functionals to guaranteed global optimality. This algorithm features a direct sequential method and builds upon a generic, spatial branch-and-bound algorithm. A new operation, called lifting, is introduced, which refines the control parameterization via a Gram-Schmidt orthogonalization process, while simultaneously eliminating control subregions that are either infeasible or that provably cannot contain any global optima. Conditions are given under which the image of the control parameterization error in the state space contracts exponentially as the parameterization order is increased, thereby making the lifting operation efficient. A computational technique based on ellipsoidal calculus is also developed that satisfies these conditions. The practical applicability of branch-and-lift is illustrated in a numerical example. © 2013 Springer Science+Business Media New York
Algorithm Engineering in Robust Optimization
Robust optimization is a young and emerging field of research having received
a considerable increase of interest over the last decade. In this paper, we
argue that the the algorithm engineering methodology fits very well to the
field of robust optimization and yields a rewarding new perspective on both the
current state of research and open research directions.
To this end we go through the algorithm engineering cycle of design and
analysis of concepts, development and implementation of algorithms, and
theoretical and experimental evaluation. We show that many ideas of algorithm
engineering have already been applied in publications on robust optimization.
Most work on robust optimization is devoted to analysis of the concepts and the
development of algorithms, some papers deal with the evaluation of a particular
concept in case studies, and work on comparison of concepts just starts. What
is still a drawback in many papers on robustness is the missing link to include
the results of the experiments again in the design
Weighted Goal Programming and Penalty Functions: Whole-farm Planning Approach Under Risk
The paper presents multiple criteria approach to deal with risk in farmer’s decisions. Decision making process is organised in a framework of spreadsheet tool. It is supported by deterministic and stochastic mathematical programming techniques applying optimisation concept. Decision making process is conceptually divided into seven autonomous modules that are mutually linked up. Beside the common maximisation of expected income through linear programming it enables also reconstruction of current production practice. Income risk modelling is based on portfolio theory resting on expected value, variance (E,V) paradigm. Modules dealing with risk are therefore supported with quadratic and constrained quadratic programming. Non-parametric approach is utilised to estimate decision maker’s risk attitude. It is measured with coefficient of risk aversion, needed to maximise certainty equivalent for analysed farms. Multiple criteria paradigm is based on goal programming approach. In contribution focus is put on benefits and possible drawbacks of supporting weighted goal programming with penalty functions. Application of the tool is illustrated with three dairy farm cases. Obtained results confirm advantage of utilizing penalty function system. Beside greater positiveness it proves as useful approach for fine tuning of the model enabling imitation of farmer’s behaviour, which is due to his/her conservative nature not perfect or rational. Results confirm hypothesis that single criteria decision making, based on maximisation of expected income, might be biased and does not necessary lead to the best - achievable option for analysed farm.goal programming, risk modelling, risk aversion, production planning, Risk and Uncertainty,
Observation of temporary accommodation for construction workers according to the code of practice for temporary construction site workers amenities and accommodation (ms2593:2015) in Johor, Malaysia
The Malaysian government is currently improving the quality of workers temporary
accommodation by introducing MS2593:2015 (Code of Practice for Temporary Site Workers
Amenities and Accommodation) in 2015. It is in line with the initiative in the Construction
Industry Transformation Programme (2016-2020) to increase the quality and well-being of
construction workers in Malaysia. Thus, to gauge the current practice of temporary
accommodation on complying with the particular guideline, this paper has put forth the
observation of such accommodation towards elements in Section 3 within MS2593:2015. A total
of seventeen (17) temporary accommodation provided by Grade 6 and Grade 7 contractors in
Johor were selected and assessed. The results disclosed that most of the temporary
accommodation was not complying with the guideline, where only thirteen (13) out of fifty-eight
(58) elements have recorded full compliance (100%), and the lowest compliance percentage
(5.9%) are discovered in the Section 3.12 (Signage). In a nutshell, given the significant gap of
compliance between current practices of temporary accommodation and MS2593:2015, a
holistic initiative need to be in place for the guideline to be worthwhile
Optimistic Robust Optimization With Applications To Machine Learning
Robust Optimization has traditionally taken a pessimistic, or worst-case
viewpoint of uncertainty which is motivated by a desire to find sets of optimal
policies that maintain feasibility under a variety of operating conditions. In
this paper, we explore an optimistic, or best-case view of uncertainty and show
that it can be a fruitful approach. We show that these techniques can be used
to address a wide variety of problems. First, we apply our methods in the
context of robust linear programming, providing a method for reducing
conservatism in intuitive ways that encode economically realistic modeling
assumptions. Second, we look at problems in machine learning and find that this
approach is strongly connected to the existing literature. Specifically, we
provide a new interpretation for popular sparsity inducing non-convex
regularization schemes. Additionally, we show that successful approaches for
dealing with outliers and noise can be interpreted as optimistic robust
optimization problems. Although many of the problems resulting from our
approach are non-convex, we find that DCA or DCA-like optimization approaches
can be intuitive and efficient
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