The demands for high flexibility in signal phase modulation, operating frequency, and amplitude modulation in contemporary wireless communication systems present challenges for reconfigurable devices. To address this ...
详细信息
The demands for high flexibility in signal phase modulation, operating frequency, and amplitude modulation in contemporary wireless communication systems present challenges for reconfigurable devices. To address this issue, two tunable couplers have been designed, which consist of two parallel microstrips couplers with oneeighth wavelength and phase-shift networks composed of two varactors and coupling lines. Adjusting the even-odd mode impedances of one-eighth wavelength parallel coupled microstrip lines has been established as a means to control the coupling. The alteration of the capacitance of the varactors allows for manipulation of the phase differences in the couplers. Considering the disparities among simulation, manufacturing, and theoretical models, to get the capacitance combinations corresponding to the specific phase differences more quickly and accurately, the linear programming solve-predict method is introduced to this design. Finally, the errors between the predicted values and the theoretical values for the actual values are compared. The relative errors of the theoretical values are not less than 31.76 %, and the maximum errors of the predicted values are not more than 12.5 %, which shows the effectiveness of the method. For demonstration, two couplers operating at 2.4 GHz with different tuning ranges were designed and fabricated, work-1 with a tuning range of 45-135 degrees and work-2 with a tuning range of 0-180 degrees degrees. Based on the standard of 10 dB return loss and 1 dB unbalanced coupling, the relative bandwidths of work-1 and work-2 are not less than 42 % and 25 %. The relative bandwidths of phase differences effective modulation (+/- 5 degrees) are 20.1-42 % and 6-25%, respectively.
In recent years, the rapid development of the city has driven the rapid upgrading of the public building industry. While the total number and scale of buildings continue to expand, its high energy consumption and high...
详细信息
In recent years, the rapid development of the city has driven the rapid upgrading of the public building industry. While the total number and scale of buildings continue to expand, its high energy consumption and high emissions also bring great pressure to the ecological environment. With the severe ecological environment situation, the formulation of effective emission reduction strategies to promote the low-carbon development of public building industry has become an urgent problem in the current urbanization process. The paper purpose of this article is to reduce building carbon emissions, enhance the actual effectiveness of emission reduction strategies, and achieve green development of public buildings. On the basis of understanding the relevant concepts, characteristics and composition of carbon emissions from public buildings, combined with the development status of carbon emissions from public buildings, this paper proposes emission reduction strategies based on linear programming and fuzzy comprehensive evaluation, and verifies them from the contribution degree, carbon emission intensity and their relationship with economic structure. The experimental results showed that the contribution of the strategy model in this paper in the public environment emission reduction could reach 0.384, which means that the strategy constructed by linear programming (LP) and fuzzy comprehensive evaluation (FCE) could effectively achieve carbon emission reduction (CER) and improve the implementation effect and efficiency of the strategy. In the construction of construction projects, the application of linear programming and fuzzy comprehensive evaluation in the carbon emission and emission reduction strategies of public buildings is of great significance for promoting environmental sustainable development and maintaining economic and ecological balance.
It is known [Mangasarian, A Newton method for linear programming, J. Optim. Theory Appl. 121 (2004), pp. 1-18] that every linear program can be solved exactly by minimizing an unconstrained quadratic penalty program. ...
详细信息
It is known [Mangasarian, A Newton method for linear programming, J. Optim. Theory Appl. 121 (2004), pp. 1-18] that every linear program can be solved exactly by minimizing an unconstrained quadratic penalty program. The penalty program is parameterized by a scalar t > 0, and one is able to solve the original linear program in this manner when t is selected larger than a finite, but unknown t(0) > 0. In this paper, we show that every linear program can be solved using the solution to a parameter-free penalty program. We also characterize the solutions to the quadratic penalty programs using fixed points of certain nonexpansive maps. This leads to an iterative thresholding algorithm that converges to a desired limit point. Weshow in numerical experiments that this iterative method can outperform a variety of standard quadratic program solvers. Finally, we show that for every t is an element of R, the solution one obtains by solving a parameterized penalty program is guaranteed to lie in the feasible set of the original linear program.
The problem of community detection with two equal-sized communities is closely related to the minimum graph bisection problem over certain random graph models. In the stochastic block model distribution over networks ...
详细信息
The problem of community detection with two equal-sized communities is closely related to the minimum graph bisection problem over certain random graph models. In the stochastic block model distribution over networks with community structure, a well-known semidefinite programming (SDP) relaxation of the minimum bisection problem recovers the underlying communities whenever possible. Motivated by their superior scalability, we study the theoretical performance of linear programming (LP) relaxations of the minimum bisection problem for the same random models. We show that, unlike the SDP relaxation that undergoes a phase transition in the logarithmic average degree regime, the LP relaxation fails in recovering the planted bisection with high probability in this regime. We show that the LP relaxation instead exhibits a transition from recovery to non-recovery in the linear average degree regime. Finally, we present non-recovery conditions for graphs with average degree strictly between linear and logarithmic.
This research is about developing a decision support system (DSS) for the distribution of grapes and grape must in a Chilean cooperative, Cooperativa Agricola Pisquera Elqui Limitada (CAPEL). CAPEL is dedicated to pro...
详细信息
This research is about developing a decision support system (DSS) for the distribution of grapes and grape must in a Chilean cooperative, Cooperativa Agricola Pisquera Elqui Limitada (CAPEL). CAPEL is dedicated to producing and distributing several beverages such as sparkling wines, beers, energy drinks, rum, and pisco. This work aims to support the grinding-related transport stage through a linear programming based DSS, in order to find the optimal use of the transport demand in a network based on source and destination plants during the harvest season. To achieve this aim, an operational research (OR) model that feeds the DSS is developed, whose objective function seeks to minimize the total transport cost. The decision variables define the grape cargo to be transported from a source plant to a destination plant. The OR model uses constraints such as transportation demand, grinding capacity, maximum storage, and available grape in plants. The model succeeded in reducing the total transport costs by 14% for the 2017 season of the pisco-making process, meaning approximately savings of 59 million Chilean pesos.
Constrained partially observable Markov decision processes (CPOMDPs) have been used to model various real-world phenomena. However, they are notoriously difficult to solve to optimality, and there exist only a few app...
详细信息
Constrained partially observable Markov decision processes (CPOMDPs) have been used to model various real-world phenomena. However, they are notoriously difficult to solve to optimality, and there exist only a few approximation methods for obtaining high-quality solutions. In this study, grid-based approximations are used in combination with linear programming (LP) models to generate approximate policies for CPOMDPs. A detailed numerical study is conducted with six CPOMDP problem instances considering both their finite and infinite horizon formulations. The quality of approximation algorithms for solving unconstrained POMDP problems is established through a comparative analysis with exact solution methods. Then, the performance of the LP-based CPOMDP solution approaches for varying budget levels is evaluated. Finally, the flexibility of LP-based approaches is demonstrated by applying deterministic policy constraints, and a detailed investigation into their impact on rewards and CPU run time is provided. For most of the finite horizon problems, deterministic policy constraints are found to have little impact on expected reward, but they introduce a significant increase to CPU run time. For infinite horizon problems, the reverse is observed: deterministic policies tend to yield lower expected total rewards than their stochastic counterparts, but the impact of deterministic constraints on CPU run time is negligible in this case. Overall, these results demonstrate that LP models can effectively generate approximate policies for both finite and infinite horizon problems while providing the flexibility to incorporate various additional constraints into the underlying model.
Based on the sequential linear programming approach for the data-driven computational mechanics considering uncertainty, a novel approach for truss optimization with displacement and stress constraints is introduced. ...
详细信息
Based on the sequential linear programming approach for the data-driven computational mechanics considering uncertainty, a novel approach for truss optimization with displacement and stress constraints is introduced. The proposed approach still capitalizes on the merits of data-driven computational mechanics, enabling optimization across various constitutive relationships by a mere replacement of the dataset. Moreover, in order to obtain the singular global optimal solution, the approach integrates the Simultaneous Analysis and Design framework, incorporating displacement as a design variable and establishing conservation law and kinematic relationship as equality constraints. In actuality, the data-driven approach is not only applicable to handling constitutive models but can also be employed to transform complex nonlinear relationships into linear combinations of data points. Consequently, the original nonlinear problem is transformed into a sequential linear programming problem. Numerical examples demonstrate that for stress-constrained truss optimization problem with the lower bound of cross-sectional area as 0, the proposed algorithm can directly yield a global optimum solution rather than a local optimal solution. In scenarios featuring linear constitutive behavior and incorporating stress and displacement constraints, both the results and efficiency yielded by this methodology closely align with traditional algorithms. Additionally, within the realm of a nonlinear constitutive model, the computational time is close to that of the linear constitutive model. In a word, the aforementioned results thoroughly demonstrate the effectiveness of the data-driven approach, providing a novel approach to solve nonlinear problems by sequential linear programming.
We consider two-stage robust linear programs with uncertain righthand side. We develop a General Polyhedral Approximation (GPA), in which the uncertainty set U is substituted by a finite set of polytopes derived from ...
详细信息
We consider two-stage robust linear programs with uncertain righthand side. We develop a General Polyhedral Approximation (GPA), in which the uncertainty set U is substituted by a finite set of polytopes derived from the vertex set of an arbitrary polytope that dominates U. The union of the polytopes need not contain U. We analyze and computationally test the performance of GPA for the frequently used budgeted uncertainty set U (with m rows). For budgeted uncertainty affine policies are known to be best possible approximations (if coefficients in the constraints are nonnegative for the second-stage decision). In practice calculating affine policies typically requires inhibitive running times. Therefore an approximation of U by a single simplex has been proposed in the literature. GPA maintains the low practical running times of the simplex based approach while improving the quality of approximation by a constant factor. The generality of our method allows to use any polytope dominating U (including the simplex). We provide a family of polytopes that allows for a trade-off between running time and approximation factor. The previous simplex based approach reaches a threshold at Gamma>root m after which it is not better than a quasi nominal solution. Before this threshold, GPA significantly improves the approximation factor. After the threshold, it is the first fast method to outperform the quasi nominal solution. We exemplify the superiority of our method by a fundamental logistics problem, namely, the Transportation Location Problem, for which we also specifically adapt the method and show stronger results.
The interval power flow (IPF) method is widely employed to address the uncertainties of renewable energy sources (RESs) in power systems. However, limited research exists on the application of mathematical optimizatio...
详细信息
The interval power flow (IPF) method is widely employed to address the uncertainties of renewable energy sources (RESs) in power systems. However, limited research exists on the application of mathematical optimization-based approaches to compute IPF results. Furthermore, a comprehensive framework for analyzing the derived IPF results and formulating appropriate countermeasures is still lacking. Therefore, this paper proposes a novel linear programming-based framework of IPF analysis for distribution systems, designed to enhance IPF calculation efficiency and keep system state variables within recommended limits utilizing controllable equipment. First, a linearized IPF model is proposed to improve calculation efficiency. The over-limit of system state variables is analysed based on the IPF results. Then, A countermeasure strategy utilizing controllable equipment is proposed to maintain system security under potential extreme scenarios. The output intervals of the controllable equipment are determined as scheduling references ensuring secure operation under the uncertainties. The numerical results demonstrate that the linearized formulation computes the IPF results 6.57 times faster than the non-linear method, with insignificant calculation errors (below 0.06 % for magnitudes and 0.02 degrees for angles). The countermeasure method can successfully keep state variables within predefined ranges and provide system operators with effective scheduling reference intervals of controllable equipment under uncertainties.
This article presents a linear optimization method for optimal placement of Electric Vehicle Charging Stations at optimal consumer charging cost in large urban networks. The optimization model is developed using Hefei...
详细信息
This article presents a linear optimization method for optimal placement of Electric Vehicle Charging Stations at optimal consumer charging cost in large urban networks. The optimization model is developed using Hefei city as a case study. Various factors such as population, population density, traffic flow, Electric vehicle penetration in transport network, road network, and electric vehicle routing are integrated to maximize the charging station utility while minimizing the consumer charging cost. To effectively handle large datasets density-based spatial clustering of applications with noise is employed to reduce data size while preserving model's computational efficiency. The optimization model is solved using linear programming by incorporating key factors such as seasonal utility grid tariffs, current consumer charging cost, traffic flow, demand density, coverage radius, and station capacity. The optimization model is validated through simulation studies on Hefei city's road network with 35% increase in electric vehicle charging station coverage and 5% reduction in consumer charging cost compared to current locations and tariff. This approach effectively identifies optimal locations for charging stations by combining urban data analytic and optimization technique. The model offers a novel solution to the challenge of planning electric vehicle charging stations and helps to advance the deployment of electric vehicle infrastructure in the city of Hefei with global sustainability goals.
暂无评论