In many practical scenarios of black box optimization, the objective function is subject to constraints that must be satisfied to avoid undesirable outcomes. Such constraints are typically unknown and must be learned ...
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In this paper, we address two main topics. First, we study the problem of minimizing the sum of a smooth function and the composition of a weakly convex function with a linear operator on a closed vector subspace. For...
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Combinatorial optimization (CO) problems arise in a wide range of fields from medicine to logistics and manufacturing. While exact solutions are often not necessary, many applications require finding high-quality solu...
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The nature inspired algorithms are becoming popular due to their simplicity and wider applicability. In the recent past several such algorithms have been developed. They are mainly bio-inspired, swarm based, physics b...
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This paper presents a novel stochastic gradient descent algorithm for constrained optimization. The proposed algorithm randomly samples constraints and components of the finite sum objective function and relies on a r...
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Bilevel optimization is characterized by a two-level optimization structure, where the upper-level problem is constrained by optimal lower-level solutions, and such structures are prevalent in real-world problems. The...
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Cloud computing has emerged as one of the hottest topics in technology and has quickly become a widely used information and communication technology model. Performance is a critical component in the cloud environment ...
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Cloud computing has emerged as one of the hottest topics in technology and has quickly become a widely used information and communication technology model. Performance is a critical component in the cloud environment concerning constraints like economic, time, and hardware issues. Various characteristics and conditions for providing solutions and designing strategies must be dealt with in different situations to perform better. For example, task scheduling and resource allocation are significant challenges in cloud management. Adopting proper techniques in such conditions leads to performance improvement. This paper surveys existing scheduling algorithms concerning the macro design idea. We classify these algorithms into four main categories: deterministic algorithms, metaheuristic algorithms, learning algorithms, and algorithms based on game theory. Each category is discussed by citing appropriate studies, and the MapReduce review is addressed as an example.
Purpose - This paper aims to Test the capabilities/accuracies of four deep learning pre trained convolutional neural network (CNN) models to detect and classify types of highway cracks, as well as developing a new CNN...
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Purpose - This paper aims to Test the capabilities/accuracies of four deep learning pre trained convolutional neural network (CNN) models to detect and classify types of highway cracks, as well as developing a new CNN model to maximize the accuracy at different learning rates. Design/methodology/approach - A sample of 4,663 images of highway cracks were collected and classified into three categories of cracks, namely, "vertical cracks," "horizontal and vertical cracks" and "diagonal cracks," subsequently, using "Matlab" to classify the sample to training (70%) and testing (30%) to apply the four deep learning CNN models and compute their accuracies. After that, developing a new deep learning CNN model to maximize the accuracy of detecting and classifying highway cracks and testing the accuracy using three optimization algorithms at different learning rates. Findings - The accuracies result of the four deep learning pre-trained models are above the averages between top-1 and top-5 and the accuracy of classifying and detecting the samples exceeded the top-5 accuracy for the pre-trained AlexNet model around 3% and by 0.2% for the GoogleNet model. The accurate model here is the GoogleNet model as the accuracy is 89.08% and it is higher than AlexNet by 1.26%. While the computed accuracy for the new created deep learning CNN model exceeded all pre-trained models by achieving 97.62% at a learning rate of 0.001 using Adam's optimization algorithm. Practical implications - The created deep learning CNN model will enable users (e.g. highway agencies) to scan a long highway and detect types of cracks accurately in a very short time compared to traditional approaches. Originality/value - A new deep learning CNN-based highway cracks detection was developed based on testing four pre-trained CNN models and analyze the capabilities of each model to maximize the accuracy of the proposed CNN.
In this paper, a new class of structured polynomials, which we dub the separable plus lower degree (SPLD in short) polynomials, is introduced. The formal definition of an SPLD polynomial, which extends the concept of ...
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The need for fast sparse optimization is emerging, e.g., to deal with large-dimensional data-driven problems and to track time-varying systems. In the framework of linear sparse optimization, the iterative shrinkage-t...
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