Results 171 to 180 of about 278,880 (218)
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Optimizing αµ

2021 IEEE Conference on Games (CoG), 2021
$αμ$ is a search algorithm which repairs two defaults of Perfect Information Monte Carlo search: strategy fusion and non locality. In this paper we optimize $αμ$ for the game of Bridge, avoiding useless computations. The proposed optimizations are general and apply to other imperfect information turn-based games.
Tristan Cazenave   +2 more
openaire   +2 more sources

Optimal harvesting and optimal vaccination

Mathematical Biosciences, 2007
Two optimization problems are considered: Harvesting from a structured population with maximal gain subject to the condition of non-extinction, and vaccinating a population with prescribed reduction of the reproduction number of the disease at minimal costs.
Hadeler, K. P., Müller, J.
openaire   +3 more sources

Optimal floorplan area optimization

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 1992
An optimal algorithm for the floorplan area optimization problem is presented. The algorithm is based on an extension of the technique of L. Stockmeyer (1983). Experimental results indicate that the authors' algorithm is efficient and capable of successfully handling large floor plans.
Ting-Chi Wang, Martin D. F. Wong
openaire   +1 more source

Optimal optimism

Business Strategy Review, 2007
A positive outlook can be a powerful force in business. But what if someone's optimism is unrealistic, unfounded or excessive?Rose Trevelyan says the results can be lethal. So, how can you be optimistic without being overconfident?
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The optimism of will

1998
Will Hutton’s book The State We’re In, published in January 1995, advocated stakeholding as an antidote to the divisive effects of unfettered markets, and the recipe for social cohesion and inclusion. It far outsold the Borrie and Rowntree Reports. By 1997, Social Justice had sold 12400 copies, and Rowntree just over 3000.
openaire   +1 more source

Multipopulation Optimization for Multitask Optimization

2019 IEEE Congress on Evolutionary Computation (CEC), 2019
Currently, the most of multitask evolutionary algorithms views multiple tasks as factors influencing the evolution of individuals. However, this consideration causes difficulty to assign fitness to individuals, because an individual which performs well on one task can have a bad performance on another task. To avoid this difficulty, this paper proposes
Zedong Tang   +4 more
openaire   +1 more source

Optimality conditions and optimization methods for quartic polynomial optimization

Applied Mathematics and Computation, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhiyou Wu   +3 more
openaire   +2 more sources

Optimism

American Journal of Roentgenology, 2008
Lauren M, Burns, Richard B, Gunderman
openaire   +4 more sources

On Optimality Conditions in Quasidifferentiable Optimization

SIAM Journal on Control and Optimization, 1984
The paper is concerned with a class of quasidifferentiable functions in the sense of \textit{V. F. Demyanov} and \textit{A. M. Rubinov} [Dokl. Akad. Nauk SSSR 250, 21-25 (Russian) (1980; Zbl 0456.49016)]. Such functions are directionally differentiable and their directional derivatives are representable as a difference of two sublinear functions.
openaire   +2 more sources

Recent Advances in Bayesian Optimization

ACM Computing Surveys, 2023
Sebastian Schmitt   +2 more
exaly  

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