104 research outputs found
SQG-Differential Evolution for difficult optimization problems under a tight function evaluation budget
In the context of industrial engineering, it is important to integrate
efficient computational optimization methods in the product development
process. Some of the most challenging simulation-based engineering design
optimization problems are characterized by: a large number of design variables,
the absence of analytical gradients, highly non-linear objectives and a limited
function evaluation budget. Although a huge variety of different optimization
algorithms is available, the development and selection of efficient algorithms
for problems with these industrial relevant characteristics, remains a
challenge. In this communication, a hybrid variant of Differential Evolution
(DE) is introduced which combines aspects of Stochastic Quasi-Gradient (SQG)
methods within the framework of DE, in order to improve optimization efficiency
on problems with the previously mentioned characteristics. The performance of
the resulting derivative-free algorithm is compared with other state-of-the-art
DE variants on 25 commonly used benchmark functions, under tight function
evaluation budget constraints of 1000 evaluations. The experimental results
indicate that the new algorithm performs excellent on the 'difficult' (high
dimensional, multi-modal, inseparable) test functions. The operations used in
the proposed mutation scheme, are computationally inexpensive, and can be
easily implemented in existing differential evolution variants or other
population-based optimization algorithms by a few lines of program code as an
non-invasive optional setting. Besides the applicability of the presented
algorithm by itself, the described concepts can serve as a useful and
interesting addition to the algorithmic operators in the frameworks of
heuristics and evolutionary optimization and computing
Benchmarking for Metaheuristic Black-Box Optimization: Perspectives and Open Challenges
Research on new optimization algorithms is often funded based on the
motivation that such algorithms might improve the capabilities to deal with
real-world and industrially relevant optimization challenges. Besides a huge
variety of different evolutionary and metaheuristic optimization algorithms,
also a large number of test problems and benchmark suites have been developed
and used for comparative assessments of algorithms, in the context of global,
continuous, and black-box optimization. For many of the commonly used synthetic
benchmark problems or artificial fitness landscapes, there are however, no
methods available, to relate the resulting algorithm performance assessments to
technologically relevant real-world optimization problems, or vice versa. Also,
from a theoretical perspective, many of the commonly used benchmark problems
and approaches have little to no generalization value. Based on a mini-review
of publications with critical comments, advice, and new approaches, this
communication aims to give a constructive perspective on several open
challenges and prospective research directions related to systematic and
generalizable benchmarking for black-box optimization
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
Mathematical methods for portfolio management
Portfolio Management is the process of allocating an investor's wealth to in
vestment opportunities over a given planning period. Not only should Portfolio
Management be treated within a multi-period framework, but one should also take into consideration
the stochastic nature of related parameters.
After a short review of key concepts from Finance Theory, e.g. utility function, risk attitude,
Value-at-rusk estimation methods, a.nd mean-variance efficiency, this work describes a framework
for the formulation of the Portfolio Management problem in a Stochastic Programming setting.
Classical solution techniques for the resolution of the resulting Stochastic Programs (e.g.
L-shaped Decompo sition, Approximation of the probability function) are presented. These are
discussed within both the two-stage and the multi-stage case with a special em phasis on the
former. A description of how Importance Sampling and EVPI are used to improve the efficiency of
classical methods is presented. Postoptimality Analysis, a sensitivity analysis method, is also
described.StatisticsM. Sc. (Operations Research
Numerical Techniques for Stochastic Optimization
This is a comprehensive and timely overview of the numerical techniques that have been developed to solve stochastic programming problems. After a brief introduction to the field, where accent is laid on modeling questions, the next few chapters lay out the challenges that must be met in this area. They also provide the background for the description of the computer implementations given in the third part of the book. Selected applications are described next. Some of these have directly motivated the development of the methods described in the earlier chapters. They include problems that come from facilities location, exploration investments, control of ecological systems, energy distribution and generation. Test problems are collected in the last chapter.
This is the first book devoted to this subject. It comprehensively covers all major advances in the field (both Western and Soviet). It is only because of the recent developments in computer technology, that we have now reached a point where our computing power matches the inherent size requirements faced in this area. The book demonstrates that a large class of stochastic programming problems are now in the range of our numerical capacities
Comparison of sea-ice freeboard distributions from aircraft data and cryosat-2
The only remote sensing technique capable of obtain- ing sea-ice thickness on basin-scale are satellite altime- ter missions, such as the 2010 launched CryoSat-2. It is equipped with a Ku-Band radar altimeter, which mea- sures the height of the ice surface above the sea level. This method requires highly accurate range measure- ments. During the CryoSat Validation Experiment (Cry- oVEx) 2011 in the Lincoln Sea, Cryosat-2 underpasses were accomplished with two aircraft, which carried an airborne laser-scanner, a radar altimeter and an electro- magnetic induction device for direct sea-ice thickness re- trieval. Both aircraft flew in close formation at the same time of a CryoSat-2 overpass. This is a study about the comparison of the sea-ice freeboard and thickness dis- tribution of airborne validation and CryoSat-2 measure- ments within the multi-year sea-ice region of the Lincoln Sea in spring, with respect to the penetration of the Ku- Band signal into the snow
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