The linear programming ( LP) based approach we introduced in [1] for finding finite blocklength converses for joint source-channel coding is extended to some network-like settings. Finite blocklength channel coding of...
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ISBN:
(纸本)9781509030972
The linear programming ( LP) based approach we introduced in [1] for finding finite blocklength converses for joint source-channel coding is extended to some network-like settings. Finite blocklength channel coding of compound and averaged channels under the maximum probability error criterion is considered. Through the LP approach new converses are obtained which imply a weak converse for both channels and a strong converse for the compound channel. The LP approach is also extended to the networked setting and a new finite blocklength converse for Slepian-Wolf coding which improves on the converse in Han [2, Lemma 7.2.2] is derived.
The pressure to maintain (or increase) the level of competitiveness in companies leads to an ever-increasing requirement for effective management supported by the variety of existing resources, which tend to interfere...
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ISBN:
(纸本)9781509050475
The pressure to maintain (or increase) the level of competitiveness in companies leads to an ever-increasing requirement for effective management supported by the variety of existing resources, which tend to interfere into the work of the manager. Particularly, in the case of waiting queues, the most relevant aspect is the combination of the number of multitasking servers assigned to attend and the demand that varies over the course of a day's work. The main objective is to meet the expectations created by the client regarding the opinion of the service that was provided. The contribution that this work proposes to give consists in planning the number of servers adapted to a given demand, not forgetting the valuation associated with the release of resources to perform other tasks related to the business. We use linear programming to determine the optimal number of the servers to serve customers, depending on their hourly availability to fulfill this function and the estimated demand. The model is submitted to four distinct real scenarios, each one reflecting a certain service reality, so that we can evaluate its behavior and possible gains against real situations occurring under the same conditions. The results show the credibility of the model since it reveals in conditions very similar to those of a real situation (translated in one of the scenarios), very little relevant differences.
A new type of linear-programming based Model Predictive Control (LP-MPC) is presented in this work. The MPC optimization process is reformulated as a linear programming problem and solved using the Simplex method. The...
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ISBN:
(纸本)9789875447547
A new type of linear-programming based Model Predictive Control (LP-MPC) is presented in this work. The MPC optimization process is reformulated as a linear programming problem and solved using the Simplex method. The optimization result is the duty cycle of a fixed-frequency pulse width modulator (PWM) which switches the transistors of a three-phase grid-connected voltage source inverter. In opposition to other LP-MPC formulations, the optimization is carried out online. In order to validate the proposal, a comparison with a Generalized Predictive Control (GPC) algorithm is made through simulations. Results show a better performance of the proposed strategy in different non ideal scenarios.
This thesis presents linear and convex programming based algorithms for NP-hard discrete optimization problems, mainly with applications in network design. Network design problems aim to find a minimal/maximal weighte...
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This thesis presents linear and convex programming based algorithms for NP-hard discrete optimization problems, mainly with applications in network design. Network design problems aim to find a minimal/maximal weighted subgraph satisfying given properties. The problems studied include maximum cut, buy-at-bulk network design, throughput maximization, and unrelated machine scheduling. This thesis considers different models of input uncertainty: the traditional deterministic setting, the online setting where inputs arrive over time and the stochastic setting where inputs are drawn from some probability distribution. Our approach to these problems involves solving suitable convex relaxations and then using rounding procedures to convert the fractional solutions to integer solutions. The specific contributions of this thesis include (1) approximation algorithms for a constrained variant of the maximum cut problem using the Sherali-Adams LP hierarchy; (2) online primal-dual algorithms for covering and packing with Lq norm objectives; (3) approximation algorithms for stochastic unrelated machine scheduling.
The definition of factor space and a unified optimization based classification model were developed for linear programming and supervised learning. Intelligent behaviour appeared in a decision process can be treated a...
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The definition of factor space and a unified optimization based classification model were developed for linear programming and supervised learning. Intelligent behaviour appeared in a decision process can be treated as a point y, the dynamic state observed and controlled by the agent, moving in a factor space impelled by the goal factor and blocked by the constraint factors. Suppose that the feasible region is cut by a group of hyperplanes, when point y reaches the region’s wall, a hyperplane will block the moving and the agent needs to adjust the moving direction such that the target is pursued as faithful as possible. Since the wall is not able to be represented to a differentiable function, the gradient method cannot be applied to describe the adjusting process. We, therefore, suggest a new model, named linear adjusting programming (LAP) in this paper. LAP is similar as a kind of relaxed linear programming (LP), and the difference between LP and LAP is: the former aims to find out the ultimate optimal point, while the latter just does a direct action in short period. You may ask: Where will a blocker encounter? How can the moving direction be adjusted? Where further blockers may be encountered next, and how should the direction be adjusted again?... If the ultimate best is found, that’s a blessing;if not, that’s fine. We request at least an adjusting should be achieved at the first time. what are the former and latter? possible to be more exact? In place of gradient vector, the projection of goal direction g in a subspace plays a core role in linear adjusting programming. If a hyperplane blocks y going ahead along with the direction d, then we must adjust the new direction d’ as the projection of g in the blocking plane. If there is only one blocker at a time, it is straightforward to calculate the projection, but how to calculate the projection when there are more than one blocker encountered simultaneously? It is an open problem for LP researchers still (M. Has
This paper presents a model that can be easily used to resolve the energy hub operation problem. The proposed model considers the uncertainty in the input and output ports. The energy hub optimum operation problem nee...
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This paper presents a model that can be easily used to resolve the energy hub operation problem. The proposed model considers the uncertainty in the input and output ports. The energy hub optimum operation problem needs to be resolved in order to supply several energy loads in combination at the minimum total energy cost. In this study, mixed-integer linear programming was applied to formulate this optimization problem. The power balance, energy storage, and converter limitations of the input and output ports were included as equality and inequality constraints. The total cost of an energy hub was determined by the price of energy carriers. Eventually, deterministic and stochastic versions of the proposed model were tested in a case study. In the test system, electricity, gas, and wind generation were used in the input port, and the output port contained the electrical and thermal load. The results demonstrated that a stochastic model based on different scenarios was more realistic than a deterministic model. However, the total operating cost of an energy hub decreased more in the stochastic model compared to the deterministic model. Published by AIP Publishing.
Bandwidth interleaving digital-to-analog converter (BI-DAC) is a new method for breaking through the bandwidth restriction of the DAC to generate a wideband signal. However, there are some errors in the BI-DAC system ...
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Bandwidth interleaving digital-to-analog converter (BI-DAC) is a new method for breaking through the bandwidth restriction of the DAC to generate a wideband signal. However, there are some errors in the BI-DAC system such as the aliasing errors caused by the non-ideal performance of the analog filters. To achieve the aliasing errors cancellation, this paper studied the minimax design of digital finite impulse response (FIR) filters. The design goal was to meet a given desired spurious free dynamic range (SFDR) of the BI-DAC system. The problem of designing the digital FIR filters was formulated as a linear programming (LP) problem which could be used to find the global optimal solution of the coefficients of the digital FIR filters. Additionally, this proposed design method performance analysis consist of the computational complexity was derived. Finally, all the proposed designs are verified by both theoretical analysis and numerical simulations, and satisfactory simulation results were achieved.
With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The ...
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With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendor-supplied treatment planning system (MultiPlan) cannot fully explore the increased beam direction search space. Besides, a sparse treatment plan (using fewer beams) is desired to improve treatment efficiency. This study proposes a singular value decomposition linear programming (SVDLP) optimization technique for circular collimator based robotic radiotherapy. The SVDLP approach initializes the input beams by simulating the process of covering the entire target volume with equivalent beam tapers. The requirements on dosimetry distribution are modeled as hard and soft constraints, and the sparsity of the treatment plan is achieved by compressive sensing. The proposed linear programming (LP) model optimizes beam weights by minimizing the deviation of soft constraints subject to hard constraints, with a constraint on the l(1) norm of the beam weight. A singular value decomposition (SVD) based acceleration technique was developed for the LP model. Based on the degeneracy of the influence matrix, the model is first compressed into lower dimension for optimization, and then back-projected to reconstruct the beam weight. After beam weight optimization, the number of beams is reduced by removing the beams with low weight, and optimizing the weights of the remaining beams using the same model. This beam reduction technique is further validated by a mixed integer programming (MIP) model. The SVDLP approach was tested on a lung case. The results demonstrate that the SVD acceleration technique speeds up the optimization by a factor of 4.8. Furthermore, the beam reduction achieves a similar plan quality to the globally optimal plan obtained by the MIP model, but is one to two orders of magnitude faster. Furthermore, the SVDLP approac
In realistic water resource planning, fuzzy constraints can be violated but still allowed to certain acceptance degrees. To address this issue, in this study, a bi-objective pseudo-interval type 2 (T2) linear programm...
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In realistic water resource planning, fuzzy constraints can be violated but still allowed to certain acceptance degrees. To address this issue, in this study, a bi-objective pseudo-interval type 2 (T2) linear programming approach with a ranking order relation between the intervals is proposed for water system allocation. This developed approach can transform normal T2 fuzzy sets, including both trapezoidal and triangular types, into the bi-objective linear programming approach solved with the proposed algorithm with mathematical rigor, which improves the flexibility of the decision supports. The new model is applied in the utilization of regional water resource management in Xiamen city, China. Concurrently, a local water system model is established by considering the aspects of industrial, agricultural, and municipal requirements. Thus, by analysis of the solution algorithm, decision-makers can obtain different optimal results by selecting different acceptance degrees. The results also demonstrate the superiority of the proposed method. Therefore, this approach not only augments the theory of the optimal allocation method in water resource management, but also provides the support for meeting the requirements of the 13th five-year plan for Xiamen ecological planning.
Mobile ad hoc networks (MANET) are wireless network without infrastructure and suffering from low power battery. Therefore the main objective in finding a route for traffic transfer from a given source to a given dest...
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Mobile ad hoc networks (MANET) are wireless network without infrastructure and suffering from low power battery. Therefore the main objective in finding a route for traffic transfer from a given source to a given destination is to minimize the node energy consumption. This paper solves the problem of finding a route satisfying the main objective of minimum energy consumption and other QoS requirements such as minimum delay and maximum packet delivery ratio by using linear programming technique. Two cases are considered: 1. The traffic amount of a given request is transmitted into single path, and 2. The traffic amount of a request can be distributed into parallel paths. A preprocessing step is done first for network topology design. This step leads to formulate the first case as integer linear programming problem and the second case as linear programming and not mixed integer linear programming. The two obtained solutions are evaluated in terms of three criteria: energy consumption, execution time, and packet delivery ratio using an experimental study. The results show that the solution of second case is much better than the first case in terms of energy consumption and execution time. Packet delivery ratio in the second case is 100% while in the first case is only 76%.
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