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Motivated by the prospects of quantum computation, the design of quantum circuits received significant attention in the recent past. Due to the complex representation of the underlying quantum mechanical phenomena, a two-stage design flow was established in which the desired functionality is first realized in terms of a reversible circuit and, afterwards, mapped into an equivalent quantum circuit...
In this paper, we consider the use of structure learning methods for probabilistic graphical models to identify statistical dependencies in high-dimensional physical processes. Such processes are often synthetically characterized using PDEs (partial differential equations) and are observed in a variety of natural phenomena. In this paper, we present ACLIME-ADMM, an efficient two-step algorithm for...
We consider the semi-supervised dimension reduction problem: given a high dimensional dataset with a small number of labeled data and huge number of unlabeled data, the goal is to find the low-dimensional embedding that yields good classification results. Most of the previous algorithms for this task are linkage-based algorithms. They try to enforce the must-link and cannot-link constraints in dimension...
Microgrids rely on energy management levels to optimally schedule their components. Conventionally, the research in this field has been focused on the optimal formulation of the generation or the demand side management separately without considering real case scenarios and validated only by simulation. This paper presents the power scheduling of a real site microgrid under a price-based demand response...
This paper examines the tracking property of nonlinear delayed multi-agent systems with impulsive effects. The strengths and locations of the stabilizing impulses, as well as the number of the controlled nodes are all assumed to be time-varying. Some sufficient criteria are established such that the considered system can exponentially track the dynamical reference state based on the given impulsive...
We give a quantum algorithm for solving semidefinite programs (SDPs). It has worst-case running time n^{\frac{1}{2}} m^{\frac{1}{2}} s^2 \poly(\log(n), \log(m), R, r, 1/δ), with n and s the dimension and row-sparsity of the input matrices, respectively, m the number of constraints, δ the accuracy of the solution, and R, r upper bounds on the size of the optimal primal and dual...
In this study, improved antlion optimization algo-rithm (IALO) is presented. The antlion optimization algorithm (ALO) is an heuristic optimization algorithm based on modeling random walks of ants and hunting ants by antlions. The random walk model of ALO and the IALO revealed by improvements in the selection method have been tested with benchmark functions with different characteristics from the literature...
Multi-objective optimization plays an important role when one has fitness functions that are somehow conflicting with each other. Also, parameter-dependent machine learning techniques can benefit from such optimization tools. In this paper, we propose a multi-objective-based strategy approach to build compact though representative training sets for Optimum-Path Forest (OPF) learning purposes. Although...
This paper addresses the parameter identification problem of a fractional order system with a known structure. Thus, based on the variational iteration method, its shown that the identification of the parameters can be formulated as an optimization problem. The objective function is the L2-norm of the error between the measured and the model outputs, and the unknown model parameters are the decision...
A lot of research has been proposed to improve network performance in the data center. However, with the development of distributed applications, these applications face a new performance bottleneck since existing solutions almost ignore the application level optimization. The concept of coflow has been proposed which provides a chance for us to optimize network in application level rather than individual...
In a closely coupled heterogeneous computing system the work is shared amongst all available computing resources. One challenge is to find an optimal division of work between the two or more very different kinds of processing units, each with their own optimal settings. We show that through the use of statistical techniques, a systematic search of the parameter space can be conducted. These techniques...
This paper tackles the problem of finding the list of solutions with strictly increasing cost for the Semi-Assignment Problem. Four different algorithms are described and compared. The first two algorithms are based on a mathematical model and on a modification of Murty's algorithm, which was designed to find the list of solutions for the classical assignment problem. The third approach is a heuristic...
Owing to its simplicity and efficacy, orthogonal matching pursuit (OMP) has been a popular sparse representation method for compressed sensing and pattern classification. As a recent extension of OMP, generalized OMP (GOMP) improves the efficiency of OMP by identifying multiple atoms each iteration. Nonetheless, GOMP utilizes the mean square error (MSE) criterion as the loss function, which has been...
Modularity is an evaluation measure for graph clustering. Louvain method is constructed by local optimization for modularity and is bottom up method as well as agglomerative hierarchical clustering. Cluster validity measures are used to evaluate cluster partitions as well as modularity. They are traditional evaluation measures in the field of clustering. We propose a novel graph clustering which is...
This paper focuses on the parallelization of TVD Method scheme for numerical time integration of evolutionary differential equations. The Hopmoc method for numerical integration of differential equations was developed aiming at benefiting from both the concept of integration along characteristic lines as well as from the spatially decomposed Hopscotch method. The set of grid points is initially decomposed...
We consider the design of computationally efficient online learning algorithms in an adversarial setting in which the learner has access to an offline optimization oracle. We present an algorithm called Generalized Followthe- Perturbed-Leader and provide conditions under which it is oracle-efficient while achieving vanishing regret. Our results make significant progress on an open problem raised by...
We present a general framework for stochastic online maximization problems with combinatorial feasibility constraints. The framework establishes prophet inequalities by constructing price-based online approximation algorithms, a natural extension of threshold algorithms for settings beyond binary selection. Our analysis takes the form of an extension theorem: we derive sufficient conditions on prices...
The commonly used preference elicitation method in decision making is the one using pairwise comparison between alternatives. In this kind of decision making scenario, an essential issue requiring attention is that of consistency, particularly in decision problems with numerous alternatives. Consistency is usually linked to the transitivity concept, which is modeled in several diverse ways. Given...
By analyzing the drivers' main consideration about how to choose a parking space, this paper is committed to path optimization of parking lot by modified Dijkstra algorithm. The main factors that impact drivers choosing optimal parking lot are driving distance, the number of vehicle moved along motorway, and the occupancy situations of free parking space. Finally, an example would be analyzed to verify...
The workforce planning helps organizations to optimize the production process with aim to minimize the assigning costs. A workforce planning problem is very complex and needs special algorithms to be solved. The problem is to select set of employers from a set of available workers and to assign this staff to the jobs to be performed. Each job requires a time to be completed. For efficiency, a worker...
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