5 edition of **Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search** found in the catalog.

- 22 Want to read
- 8 Currently reading

Published
**December 31, 1997**
by Springer
.

Written in English

- Computer modelling & simulation,
- Operational research,
- Operations Research,
- Operations Research (Engineering),
- Optimization (Mathematical Theory),
- Technology,
- Business / Economics / Finance,
- Science/Mathematics,
- Decision Making & Problem Solving,
- General,
- Management Science,
- Business & Economics / Management Science,
- Business & Economics / Operations Research,
- Business & Economics : Decision Making & Problem Solving,
- Computers : General,
- Logic programming,
- Mathematical optimization

The Physical Object | |
---|---|

Format | Hardcover |

Number of Pages | 320 |

ID Numbers | |

Open Library | OL7810098M |

ISBN 10 | 0792380789 |

ISBN 10 | 9780792380788 |

the many stochastic methods using information such as gradients of the loss function. Section discusses some general issues in stochastic optimization. Section discusses random search methods, which are simple and surprisingly powerful in many applications. Section discusses stochastic approximation,File Size: 1MB. Read "Advances in Combinatorial Optimization Linear Programming Formulations of the Traveling Salesman and Other Hard Combinatorial Optimization Problems" by Moustapha Diaby available from Rakuten Kobo. Combinational optimization (CO) is a topic in applied mathematics, decision science and computerBrand: World Scientific Publishing Company.

Stochastic programming is one framework for taking the stochastic nature of the data into account when formulating and solving an optimization problem. In stochastic programming formulations, decisions are divided into those that need to be made \here and now" and those that can be made after the values of the random variables become known. This book is concerned with the third class of algorithms, from both a theoretical and practical point of view. It introduces stochastic local search algorithms as the choice when solving really hard problems. The book begins by accurately describing the different types of problems, and existing techniques for solving them.

Ling Zhang, Bo Zhang, in Quotient Space Based Problem Solving, Abstract. Heuristic search is a graph search procedure which uses heuristic information from sources outside the graph. But for many known algorithms, the computational complexity depends on the precision of the heuristic estimates, and for lack of global view in the search process the exponential . $\begingroup$ Stochastic optimization is the bigger field of study where stochastic programming follows specific models $\endgroup$ – User Dec 9 '17 at $\begingroup$ Thanks @User, can you elaborate and maybe provide an example of what you mean by specific models in SP (and perhaps the more general concept explored in SO)?

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The research presented in the volume is evidence of the expanding frontiers of these two intersecting disciplines and provides researchers and practitioners with new work in the areas of logic programming, stochastic optimization, heuristic search and. Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search Search within book.

Front Matter. of these two intersecting disciplines and provides researchers and practitioners with new work in the areas of logic programming, stochastic optimization, heuristic search and post-solution analysis for.

Book Selection; Published: 05 February ; Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search. DL Woodruff (ed.). Kluwer Academic Publishers, London, vii + pp.

£ ISBN 0 9. J M Wilson 1Cited by: Get this from a library. Advances in computational and stochastic optimization, logic programming, and heuristic search: interfaces in computer science and operations research.

[David L. Get this from a library. Advances in Computational and Stochastic Optimization in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search: Interfaces in Computer Science and Operations Research.

[David L Woodruff] -- Computer Science and Operations Research continue to have a synergistic relationship and this book - as a part of the Operations Research and Computer Science.

Contribution to Book Constraint satisfaction methods for generating valid cuts Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search (). Advances in Stochastic and Deterministic Global Optimization (Springer Optimization and Its Applications Book ) - Kindle edition by Pardalos, Panos M., Zhigljavsky, Anatoly, Žilinskas, Julius.

Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Advances in Stochastic and Manufacturer: Springer. This book presents the latest findings on stochastic dynamic programming models and on solving optimal control problems in networks.

It includes the authors’ new findings on determining the optimal solution of discrete optimal control problems in networks and on solving game variants of Markov decision problems in the context of computational : Dmitrii Lozovanu, Stefan Pickl. Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search (ICS ), ; A linear programming framework for logics of uncertainty (author(s): John Hooker, K.

Andersen) Decision Support Syst ; An Annotated Bibliography for Post-solution Analysis in Mixed Integer Programming and Combinatorial Optimization, in Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search, D.L.

Woodruff (ed.), Kluwer Academic Publishers, Boston, MA, Combining stochastic and heuristic search to improve model-based process control algorithms 1 Introduction In this paper, the application of heuristics – namely experiences of plant operators – to. Many people do not realize that a stochastic algorithm is nothi ng else than a random search, with hints by a chosen heuristic s (or m eta-heuristics) to guide the next potential solution to evaluate.

Motivation and a simple example. Suppose that, ∈ [,] is given, and we wish to compute ×.Stochastic computing performs this operation using probability instead of arithmetic. Specifically, suppose that there are two random, independent bit streams called stochastic numbers (i.e.

Bernoulli processes), where the probability of a one in the first stream is, and. Book Description. Adaptive Stochastic Optimization Techniques with Applications provides a single, convenient source for state-of-the-art information on optimization techniques used to solve problems with adaptive, dynamic, and stochastic features.

Presenting modern advances in static and dynamic optimization, decision analysis, intelligent systems, evolutionary programming. From stochastic search to dynamic programming. Stochastic search is itself an umbrella term that encompasses derivative-based search (stochastic gradient methods, stochastic approximation methods), and derivative-free search (which includes a lot of the work in the simulation-optimization community, and the black-box optimization community).

Computational Effort As can be seen above, it is difficult to evaluate the performance of stochastic algorithms, because, as Koza explains for genetic programming in (Koza, ): Since genetic programming is a probabilistic algorithm, not all runs are successful at yielding a solution to the problem by by: Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element (with regard to some criterion) from some set of available alternatives.

Optimization problems of sorts arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods. The disciplines of Computer Science and Operations Research have been linked since their origins and each have contributed to the dramatic advances of the other.

This volume examines some of the recent advances resulting from the confluence between these two technical communities. In the process Price: $ PH is as a heuristic, with the objective of quickly locating high-quality solutions. PH for Problem (L) Given λ Remark 1 enables modiﬁcation of the PH algorithm given above in § by adding the logic If c(x(s)(k)) ≤ λ then d s:= 1 else d s:= 0 to Steps 2 and 6.

The result is that for a givenλ, a straightforward PH algorithm for. An Annotated Bibliography for Post-Solution Analysis in Mixed Integer Programming and Combinatorial Optimization.

Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search, Cited by:. Optimization and Computational logic (Wiley Series in Discrete Mathematics and Optimization) [E-Book D.o.w.n.l.o.a.d] Optimization and Computational logic (Wiley Series in Discrete Mathematics and Optimization) [R.E.A.D O.n.L.i.n.e] Optimization and Computational logic (Wiley Series in Discrete Mathematics and Optimization) [F'u'l'l E-Book].In D.L.

Woodruff, ed., Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search: Interfaces in Computer Science and Operations Research, Kluwer Academic Publishers (Dordrecht, The Netherlands, ) Stochastic optimization model.

Solution algorithm: stochastic dual dynamic programming (SDDP) Avoids “curse of dimensionality” of traditional SDP ⇒handles large systems Suitable for distributed processing.

Stochastic parameters Hydro inflows and renewable generation (wind, solar, biomass etc.) Multivariate stochastic model (PAR(p)).