In the contemporary business environment, it is crucial to have an efficient and precise response generation system to build client trust, optimize operations, and provide customized solutions. Current response manage...
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This paper studies a class of simple bilevel optimization problems where we minimize a composite convex function at the upper-level subject to a composite convex lower-level problem. Existing methods either provide as...
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This paper studies a class of simple bilevel optimization problems where we minimize a composite convex function at the upper-level subject to a composite convex lower-level problem. Existing methods either provide asymptotic guarantees for the upper-level objective or attain slow sublinear convergence rates. We propose a bisection algorithm to find a solution that is ϵf-optimal for the upper-level objective and ϵg-optimal for the lower-level objective. In each iteration, the binary search narrows the interval by assessing inequality system feasibility. Under mild conditions, the total operation complexity of our method is Õ ( max{pLf1/ϵfpLg1/ϵg} ) . Here, a unit operation can be a function evaluation, gradient evaluation, or the invocation of the proximal mapping, Lf1 and Lg1 are the Lipschitz constants of the upper- and lower-level objectives’ smooth components, and Õ hides logarithmic terms. Our approach achieves a near-optimal rate, matching the optimal rate in unconstrained smooth or composite convex optimization when disregarding logarithmic terms. Numerical experiments demonstrate the effectiveness of our method. Copyright 2024 by the author(s).
Floods are prevalent disasters in the United States, posing escalating risks due to climate change -induced factors like rising sea levels and erratic rainfall patterns. Despite governmental efforts, flood risk commun...
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Accurate motion forecasting for traffic agents is crucial for ensuring the safety and efficiency of autonomous driving systems in dynamically changing environments. Mainstream methods adopt a one-query-one-trajectory ...
When there are outliers or heavy-tailed distributions in the data, the traditional least squares with penalty function is no longer applicable. In addition, with the rapid development of science and technology, a lot ...
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When there are outliers or heavy-tailed distributions in the data, the traditional least squares with penalty function is no longer applicable. In addition, with the rapid development of science and technology, a lot of data, enjoying high dimension, strong correlation and redundancy, has been generated in real life. So it is necessary to find an effective variable selection method for dealing with collinearity based on the robust method. This paper proposes a penalized M-estimation method based on standard error adjusted adaptive elastic-net, which uses M-estimators and the corresponding standard errors as weights. The consistency and asymptotic normality of this method are proved theoretically. For the regularization in high-dimensional space, the authors use the multi-step adaptive elastic-net to reduce the dimension to a relatively large scale which is less than the sample size, and then use the proposed method to select variables and estimate parameters. Finally, the authors carry out simulation studies and two real data analysis to examine the finite sample performance of the proposed method. The results show that the proposed method has some advantages over other commonly used methods.
Real-time prediction of solar energy production is a pivotal element in effectively managing energy grids. This research embarks on a pioneering exploration into the predictive modeling of solar energy potential, cent...
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Cardiovascular disorders remain leading cause for mortality worldwide, necessitating robust early risk assessment. Although machine learning models show promise, most rely on conventional preprocessing, which lacks mo...
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Batch process is an indispensable part of modern industrial production. Because of its flexibility and high efficiency,it is widely used in biopharmaceutical,wastewater treatment,fine chemical and other fields. Due to...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
Batch process is an indispensable part of modern industrial production. Because of its flexibility and high efficiency,it is widely used in biopharmaceutical,wastewater treatment,fine chemical and other fields. Due to the improvement of modern equipment and the expansion of the scale of the factory,the chance of accidents and failures also showed an exponential increase trend. Therefore,fault diagnosis is very important to ensure the stability and safety of chemical processes. In order to improve the safety and reliability of batch process,an improved Genetic Algorithm is proposed to optimize the parameters of Separable Convolution Network and Temporal Convolutional Network(SeparableConv1D-TCN) for fault diagnosis of batch process. Firstly,the SeparableConv1D-TCN network is constructed to extract features from the original intermittent data and perform fault diagnosis by standardizing the intermittent data. In order to find the network parameters with high diagnostic accuracy,the genetic algorithm with good point set method and optimal domain search is used to optimize. The effectiveness of the method is verified by simulation experiments and comparative experiments with penicillin experimental data.
We study the detection of beyond-quantum nonlocal states that can exist in a theoretical model whose local systems are standard quantum theory in the framework of general probabilistic theories (GPTs). We find that de...
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We study the detection of beyond-quantum nonlocal states that can exist in a theoretical model whose local systems are standard quantum theory in the framework of general probabilistic theories (GPTs). We find that device-dependent detections are possible for beyond-quantum nonlocal states in GPTs even though device-independent detections are not valid. We give a device-dependent detection based on local observables to distinguish any beyond-quantum nonlocal state from all standard quantum states. In particular, we give a way to detect any beyond-quantum nonlocal state of the two-qubit system by observing only spin observables on local systems. Our results will help in the experimental detection of beyond-quantum nonlocality or justification of standard quantum theory.
In this paper we present an interactive visualization system for solving IEEE VAST Challenge 2024 Mini-Challenge 1. Our system enables interactive exploration and mining of the knowledge graph, assists in identifying ...
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