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Stochastic Gradient Descent (SGD) is the method of choice for large scale problems, most notably in deep learning. Recent studies target improving convergence and speed of the SGD algorithm. In this paper, we equip the SGD algorithm and its advanced versions with an intriguing feature, namely handling constrained problems. Constraints such as orthogonality are pervasive in learning theory. Nevertheless...
Orthogonal frequency division multiple (OFDM) has been introduced into long term evolution (LTE) because of its high spectral efficiency and robust anti-multipath fading ability. However, a major drawback of OFDM signals is high fluctuations of signal envelope. Peak-to-average power ratio (PAPR) is a well-known measure for the envelope fluctuations. Recently, another metric named cubic metric (CM)...
One popular approach for blind deconvolution is to formulate a maximum a posteriori (MAP) problem with sparsity priors on the gradients of the latent image, and then alternatingly estimate the blur kernel and the latent image. While several successful MAP based methods have been proposed, there has been much controversy and confusion about their convergence, because sparsity priors have been shown...
In this work, super twisting controller is tested for paraplegic knee stimulation. In contrary to the literature controllers proposed for the paraplegic patient, our command assures a uniform global convergence using the Lyapunov function. We studied, firstly, the performances of the controller by comparing them with those of literature. Then, we proposed an adaptive super twisting controller assuring...
We address the problem of distance metric learning (DML), defined as learning a distance consistent with a notion of semantic similarity. Traditionally, for this problem supervision is expressed in the form of sets of points that follow an ordinal relationship – an anchor point x is similar to a set of positive points Y , and dissimilar to a set of negative points Z, and a loss defined over these...
We present a novel online learning paradigm for nonlinear function estimation based on iterative orthogonal projections in an L2 space reflecting the stochastic property of input signals. An online algorithm is built upon the fact that any finite dimensional subspace has a reproducing kernel, which is given in terms of the Gram matrix of its basis. The basis used in the present study involves multiple...
Latent Dirichlet Allocation has been developed as topic-based method which uses reasoning to determine the topics of a document. There are many methods of reasoning used for Latent Dirichlet Allocation, including the Gibbs Sampling and Mean Variational Inference, the most widely used in research. However, there have not been many studies that discuss the implementation of these methods on the Indonesian...
Consider a data source comprised of a graph with marks on its edges and vertices. Examples of such data sources are social networks, biological data, web graphs, etc. Our goal is to design schemes that can efficiently compress and store such data. We aim for universal compression, i.e. without making assumptions about the stochastic properties of the data. To make sense of this, we employ the framework...
Interactive multiobjective optimization (IMO) methods aim at supporting human decision makers (DMs) to find their most preferred solutions in solving multiobjective optimization problems. Due to the subjectivity of human DMs, human fatigue, or other limiting factors, it is hard to design experiments involving human DMs to evaluate and compare IMO methods. In this paper, we propose a framework of a...
Vehicle Routing Problem (VRP) is a widely known NP-Hard combinatorial optimization problem. This paper presents a proposal of a memetic algorithm (MA) with simulated annealing (SA) as trajectory-based method for solving the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). A novel crossover operator, the Single Breaking-point Sequence Based Crossover (SBSBX), is introduced and compared...
Energy efficiency (EE) is becoming one of the important criteria in wireless transmission design. This paper discusses the recently proposed energy-efficient transmit beamforming designs for multicell multiuser multiple-input single-output (MISO) systems, including maximizing overall network EE, sum weighted EE and fairness EE. Generally, the EE optimization problems are NP-hard nonconvex programs...
A permutation flowshop scheduling problem of optimizing the makespan and the total flow time, which can be expressed as Fm|prum|(Cmax, ΣCi), is considered in this paper. An improved multi-objective memetic algorithm (IMOMA) is proposed due to the NP-hardness of the problem. In order to effectively trade-off between two objectives, we propose a NEH and LR heuristic based initialization strategy and...
This paper studies linear set-dynamics driven by random convex compact sets (RCCSs), where the parameter matrix evolves according to an underlying Markovian random process taking values in a finite set. We derive dynamics of the expectations of the associated reach sets. We establish that such expectations evolve according to coupled deterministic set-dynamics. We provide sufficient conditions for...
Diffeomorphic image registration algorithms are widely used in medical imaging, and require optimization of a high-dimensional nonlinear objective function. The function being optimized has many characteristics that are relevant for optimization but are typically not well understood. Due to that complexity, most authors have used a simple gradient descent, but it is not often discussed how step sizes...
Background/Objective: Interior gateway protocols are used for communication between devices in an organizational network. The setup which includes routers, switches and hosts is simulated in GNS3 software to represent a complex real time enterprise level network. Each protocol is implemented in the designed topology. Methods/Statistical analysis: Interior gateway protocols-RIP (Routing Information...
Person re-identification remains a challenging problem due to large variations of poses, occlusions, illumination and camera views. To learn both feature representation and similarity metric simultaneously, deep metric learning methods using triplet convolutional neural network have been applied in person re-identification. In this paper, we propose a body structure based triplet convolutional neural...
Recently, there have been growing interests in solving distributed consensus optimization problems over directed networks that consist of multiple agents. In this paper, we develop a first-order (gradient-based) algorithm, referred to as Push-DIGing, for this class of problems. To run Push-DIGing, each agent in the network only needs to know its own out-degree and employs a fixed step-size. Under...
This paper presents an accurate design of stable, minimum phase, and wideband digital rational approximations to the Fractional Order Integrators (FOIs) based on a recently proposed nature-inspired metaheuristic optimization approach motivated by the intelligent manners of crows called Crow Search Algorithm (CSA). The proposed CSA based Digital Fractional Order Integrators (DFOIs) are compared with...
Real-world networks are known to exhibit community structure, characterized by presence of dense node clusters with loose edge connections among them. Although identification of communities is a well-studied subject, most approaches only focus on edge-based criteria which may not incorporate important grouping information captured by higher-order structures e.g., cliques and cycles, to name a few...
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