linear programming(LP) is often used in reality production and life, such as the use of resources, human resource management, production arrangement. The client should pay out huge amounts of overhead to dispose huge ...
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linear programming(LP) is often used in reality production and life, such as the use of resources, human resource management, production arrangement. The client should pay out huge amounts of overhead to dispose huge data sets with its resource-constraint devices. Fortunately, Cloud computing can finish off this deficiency. The client outsources the computation to cloud servers. Then the servers accomplish the task and return the result to the client. But in the process, the correctness, verifiability and privacy should be emphasized. In our paper, based on the studies and analysis of previous protocols for secure outsourcing of linear programming in cloud computing, we find the obvious deficiencies in efficiency and safety. On the basis of the existing protocols, we propose a new protocol that combines the advantages of existing protocols and improves the shortcomings of existing protocols. We also analyze the security and efficiency of the new protocol and design a suitable simulation to verify the efficiency of this protocol.
This paper presents a numerical algorithm for computing ISS Lyapunov functions for discrete-time systems which are input-to-state stable (ISS) on compact subsets of the state space. The algorithm relies on solving a l...
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This paper presents a numerical algorithm for computing ISS Lyapunov functions for discrete-time systems which are input-to-state stable (ISS) on compact subsets of the state space. The algorithm relies on solving a linear optimisation problem and delivers a continuous and piecewise affine ISS Lyapunov function on a suitable triangulation covering the given compact set excluding a small neighbourhood of the origin. The objective of the linear optimisation problem is to minimise the ISS gain. It is shown that for every ISS system there exists a suitable triangulation such that the proposed algorithm terminates successfully. (C) 2016 Elsevier Inc. All rights reserved.
This paper proposes hybrid Genetic Algorithm (GA) - Interval linear programming (ILP) approach to optimal relay coordination problem for microgrids. Relay coordination in microgrids is complex because of varied and bi...
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This paper proposes hybrid Genetic Algorithm (GA) - Interval linear programming (ILP) approach to optimal relay coordination problem for microgrids. Relay coordination in microgrids is complex because of varied and bidirectional fault currents because of the existence of Distributed Generation (DG). Overcurrent relays are the feasible and economic choice of protection for meshed distribution systems. The coordinated relay settings must account for various possible fault scenarios in both grid connected and isolated microgrid modes of operation. Inadequate fault current levels from grid connected to the isolated mode are the major cause of protection miscoordination. This paper systematically formulates the relay coordination problem for microgrids as a linear interval optimization problem and introduces a new method of solution using hybrid GA - ILP method. The challenge in using GA to find the optimum in less number of iterations and the feasibility of ILP method to include various fault scenarios as “uncertain but bounded” intervals are combined to find the optimal overcurrent relay settings. The results show the effectiveness of proposed method. The programming is done using optimization tool box available in Matlab.
We propose a Mizuno-Todd-Ye predictor-corrector infeasible-interior-point method for linear programming over symmetric cones by using a wide neighborhood. In the corrector step, we adopt a special strategy, which can ...
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We propose a Mizuno-Todd-Ye predictor-corrector infeasible-interior-point method for linear programming over symmetric cones by using a wide neighborhood. In the corrector step, we adopt a special strategy, which can ensure the existence of a step size to keep every iteration in the given small neighborhood. By using an elegant analysis, we obtain the iteration bounds for a commutative class of directions. In particular, the iteration bound is for the Nesterov-Todd search direction, and for the xs and sx search direction. To our knowledge, the obtained iteration bounds match the currently best known iteration bounds for infeasible-interior-point method. Some preliminary numerical results are provided as well.
linear programming is the core problem of various operational research *** dominant approaches for linear programming are simplex and interior point *** this paper,we showthat the alternating direction method of multi...
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linear programming is the core problem of various operational research *** dominant approaches for linear programming are simplex and interior point *** this paper,we showthat the alternating direction method of multipliers(ADMM),which was proposed long time ago while recently found more and more applications in a broad spectrum of areas,can also be easily used to solve the canonical linear programming *** resulting per-iteration complexity is O(mn)where m is the constraint number and n the variable *** each iteration,there are m subproblems that are eligible for parallel computation;each requiring only O(n)*** is no inner iteration as *** thus introduce the newADMMapproach to linear programming,which may inspire deeper research for more complicated scenarios with more sophisticated results.
This article presents an approximation scheme for the infinite-dimensional linear programming formulation of discrete-time Markov control processes via a finite-dimensional convex program, when the dynamics are unknow...
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This article presents an approximation scheme for the infinite-dimensional linear programming formulation of discrete-time Markov control processes via a finite-dimensional convex program, when the dynamics are unknown and learned from data. We derive a probabilistic explicit error bound between the data-driven finite convex program and the original infinite linear program. We further discuss the sample complexity of the error bound which translates to the number of samples required for an a priori approximation accuracy. Our analysis sheds light on the impact of the choice of basis functions for approximating the true value function. Finally, the relevance of the method is illustrated on a truncated LQG problem.
In this study, a simple deterministic water allocation model was developed to optimally allocate limited available water resources among different water-use sectors. The model considers two single-objective functions ...
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In this study, a simple deterministic water allocation model was developed to optimally allocate limited available water resources among different water-use sectors. The model considers two single-objective functions and one multi-objective function. The first single objective (B0W1) optimizes the satisfaction levels among various water demand sectors, whereas the second single objective (B1W0) maximizes net economic benefits. The multi-objective (B1W1) combines the first two single objectives. For the multi-objective function, the model considers two optimization techniques, a simultaneous compromise constraint technique and a weighting technique to optimize both the satisfaction level and economic benefits. The model is applied to the Hingol River basin in the Baluchistan Province of Pakistan. To evaluate the model's applicability under different situations, different schemes are applied to consider variations in the minimum satisfaction level and to assign priorities to various water-use sectors. The results indicate that the value of economic benefits obtained by B1W1 lies between B0W1 and B1W0. This is a compromise between the two individual objectives. The model is easy to adopt under different conditions, because of its simplicity and flexibility.
linear programming is now included in algorithm undergraduate and postgraduate courses for computer science majors. We give a self-contained treatment of an interior-point method which is particularly tailored to the ...
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linear programming is now included in algorithm undergraduate and postgraduate courses for computer science majors. We give a self-contained treatment of an interior-point method which is particularly tailored to the typical mathematical background of CS students. In particular, only limited knowledge of linear algebra and calculus is assumed. (C) 2016 Elsevier Inc. All rights reserved.
Prefilter synthesis is one of the important design steps of Horowitz's quantitative feedback theory (QFT) to robust feedback system synthesis. The prefilter is designed to achieve tracking specifications. In this ...
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With Docker gaining widespread popularity in the recent years, the container scheduler becomes a crucial role for the exploding containerized applications and services. In this work, the container host energy conserva...
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ISBN:
(纸本)9781509063079
With Docker gaining widespread popularity in the recent years, the container scheduler becomes a crucial role for the exploding containerized applications and services. In this work, the container host energy conservation, the container image pulling costs from the image registry to the container hosts and the workload network transition costs from the clients to the container hosts are evaluated in combination. By modeling the scheduling problem as an integer linear programming, an effective and adaptive scheduler is proposed. Impressive cost savings were achieved compared to Docker Swarm scheduler. Moreover, it can be easily integrated into the open-source container orchestration frameworks.
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