Titlebook: Constraint Programming and Decision Making: Theory and Applications; Martine Ceberio,Vladik Kreinovich Book 2018 Springer International Pu

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Automatic Loop-Shaping of ,/, Problem in QFT Using Interval Consistency Based Hybrid Optimization,onsistent values, which are not part of the solution in the controller parameter regions. The hybrid part merges interval global optimization and nonlinear local optimization methods. The performance of the proposed algorithm is compared with the existing interval methods for this ./. problem example.

敲诈

Comparisons of Measurement Results as Constraints on Accuracies of Measuring Instruments: When can ssible; however, can uniquely determine the corresponding probability distributions if we have several state-of-the-art measuring instruments, and for one of them, the corresponding probability distribution is symmetric.

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Why Deep Neural Networks: A Possible Theoretical Explanation,llelism. Recently, however, it was empirically shown that, in spite of this argument, multi-layer (“deep”) neural networks leads to a much more efficient machine learning. In this paper, we provide a possible theoretical explanation for the somewhat surprising empirical success of deep networks.

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Towards a Physically Meaningful Definition of Computable Discontinuous and Multi-valued Functions (physical phenomena are described by discontinuous or multi-valued functions (a.k.a. constraints). In this paper, we provide a physics-motivated definition of computable discontinuous and multi-valued functions, and we analyze properties of this definition.

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Existence of the Nash-Optimal Strategies in the Meta-Game, such where the players are the same agents as in the original oligopoly but now using the conjectures as their strategies. Then the consistent conjectures of the original oligopoly game provide for the Cournot-Nash optimal strategies for the meta-game.

羞辱

Uncertainties in Greenhouse Gas Inventoriesrly, we specifically put irrational decisions in opposition to force one out. In doing so, we aim to enable the prediction of decisions that are themselves fair and rational. Our model is illustrated on a toy example.

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https://doi.org/10.1007/978-3-319-16136-55) method is used, in which we maximize the number of cases in which the model’s prediction is within the 25% range of the observations. In this paper, we show that even for linear models, pred(25) parameter estimation is NP-hard.

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Emerging Developments and Future Questions,onsistent values, which are not part of the solution in the controller parameter regions. The hybrid part merges interval global optimization and nonlinear local optimization methods. The performance of the proposed algorithm is compared with the existing interval methods for this ./. problem example.