This paper introduces a novel approach to blind adaptive equalization for digital communication systems using genetic algorithms (GAs). Unlike traditional methods that rely on linear programming and suffer from local ...
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This paper introduces a novel approach to blind adaptive equalization for digital communication systems using genetic algorithms (GAs). Unlike traditional methods that rely on linear programming and suffer from local minima issues, this technique utilizes a stochastic linear programming cost function with GAs for robust optimization. The proposed method termed Blind linear Equalizer based on genetic algorithm (BLE-GA) enhances performance by leveraging a GA's ability to handle stochastic variables, offering rapid convergence and resilience against signal noise and inter-symbol interference. Extensive simulations demonstrate the effectiveness of BLE-GA across different QAM systems, outperforming conventional techniques like the Constant Modulus Algorithm in scenarios with high modulation levels. This study validates the potential of using GAs in adaptive blind equalization to achieve reliable and efficient communication, even in complex and noisy channel conditions.
Subset selection, which refers to the selection of a finite number of variables to optimize a given objective function, is a fundamental problem in various applications. Among the existing algorithms for solving this ...
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A numerical method is proposed for a class of one-dimensional stochastic control problems with unbounded state space. This method solves an infinite-dimensional linear program, equivalent to the original formulation b...
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A numerical method is proposed for a class of one-dimensional stochastic control problems with unbounded state space. This method solves an infinite-dimensional linear program, equivalent to the original formulation based on a stochastic differential equation, using a finite element approximation. The discretization scheme itself and the necessary assumptions are discussed, and a convergence argument for the method is presented. Its performance is illustrated by examples featuring long-term average and infinite horizon discounted costs, and additional optimization constraints.
In tunnel geological forecasting, the electrical resistivity inversion method is extensively employed due to its high sensitivity to water-bearing bodies. Traditional inversion methods, such as least squares, simplify...
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In tunnel geological forecasting, the electrical resistivity inversion method is extensively employed due to its high sensitivity to water-bearing bodies. Traditional inversion methods, such as least squares, simplify nonlinear problems into linear ones. However, they often converge to local minima, making it challenging to identify the global optimal solution, and their inversion results are highly dependent on the choice of the initial model. To address these challenges, we propose integrating convolutional neural networks (CNNs) into the conventional iterative inversion framework. Instead of directly optimizing the initial resistivity model, our approach focuses on updating the network parameters, with the resistivity model subsequently generated by the CNN. This enables the CNN structure to regularize the resistivity model, resulting in a smoother objective function. Consequently, our method exhibits greater robustness to variations in the initial model, leading to improved inversion results. Our numerical simulations and practical applications in engineering projects demonstrate that, compared to traditional inversion methods, the proposed approach is less sensitive to the initial model and achieves superior inversion outcomes, thereby validating our hypothesis.
Neural dynamics is a powerful tool to solve online optimization problems and has been used in many applications. However, some problems cannot be modelled as a single objective optimization and neural dynamics method ...
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Neural dynamics is a powerful tool to solve online optimization problems and has been used in many applications. However, some problems cannot be modelled as a single objective optimization and neural dynamics method does not apply. This paper proposes the first neural dynamics model to solve bi-objective constrained quadratic program, which opens the avenue to extend the power of neural dynamics to multi-objective optimization. We rigorously prove that the designed neural dynamics is globally convergent and it converges to the optimal solution of the bi-objective optimization in Pareto sense. Illustrative examples on bi-objective geometric optimization are used to verify the correctness of the proposed method. The developed model is also tested in scientific computing with data from real industrial data with demonstrated superior to rival schemes.
The open-loop Stackelberg game is conceptually extended to p players by the multilevel programming problem (MLPP) and can thus be used as a model for a variety of hierarchical systems in which sequential planning is...
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The open-loop Stackelberg game is conceptually extended to p players by the multilevel programming problem (MLPP) and can thus be used as a model for a variety of hierarchical systems in which sequential planning is the norm. The rational reaction sets for each of the players is first developed, and then the geometric properties of the linear MLPP are stated. Next, first-order necessary conditions are derived, and the problem is recast as a standard nonlinear program. A cutting plane algorithm using a vertex search procedure at each iteration is proposed to solve the linear three-level case. An example is given to highlight the results, along with some computational experience.
We introduce a new algorithm based on linear programming for optimization of average-cost Markov decision processes (MDPs). The algorithm approximates the differential cost function of a perturbed MDP via a linear com...
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We introduce a new algorithm based on linear programming for optimization of average-cost Markov decision processes (MDPs). The algorithm approximates the differential cost function of a perturbed MDP via a linear combination of basis functions. We establish a bound on the performance of the resulting policy that scales gracefully with the number of states without imposing the strong Lyapunov condition required by its counterpart in de Farias and Van Roy (de Farias, D. R, B. Van Roy. 2003. The linear programming approach to approximate dynamic programming. Oper Res. 51(6) 850-865]. We investigate implications of this result in the context of a queueing control problem.
This article proposes a second-order conic programming (SOCP) approach to solve distributionally robust two-stage linear programs over 1-Wasserstein balls. We start from the case with distribution uncertainty only in ...
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This article proposes a second-order conic programming (SOCP) approach to solve distributionally robust two-stage linear programs over 1-Wasserstein balls. We start from the case with distribution uncertainty only in the objective function and then explore the case with distribution uncertainty only in constraints. The former program is exactly reformulated as a tractable SOCP problem, whereas the latter one is proved to be generally NP-hard as it involves a norm maximization problem over a polyhedron. However, it reduces to an SOCP problem if the extreme points of the polyhedron are given as a prior. This motivates the design of a constraint generation algorithm with provable convergence to approximately solve the NP-hard problem. Moreover, the least favorable distribution achieving the worst case cost is given as an ``empirical'' distribution by simply perturbing each original sample for both cases. Finally, experiments illustrate the advantages of the proposed model in terms of the out-of-sample performance and computational complexity.
linear programs with quadratic ("ridge") regularization are of recent interest in optimal transport: unlike entropic regularization, the squared-norm penalty gives rise to sparse approximations of optimal tr...
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Proponents of sustainable mobility often endorse car-sharing because it provides opportunities for circular economic strategies while preserving ownership and minimizing resource use. Influenced by consumer willingnes...
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Proponents of sustainable mobility often endorse car-sharing because it provides opportunities for circular economic strategies while preserving ownership and minimizing resource use. Influenced by consumer willingness, car-sharing services can provide equivalent transportation with fewer vehicles, reduce raw material procurement, and lower industry reliance on certain nonrenewable resources. However, interindustry linkages in upstream supply chains can cause demand shifts, potentially decreasing the economic benefits for raw material-related industries. This study aims to develop a methodology that combines linear programming and a computable general equilibrium model to assess how consumer behavior affects the potential of car-sharing to replace private car usage in densely populated cities such as those in Taiwan and how the resultant potential influences the repercussion effect across industries. The analysis examines tradeoffs between value-added, resource demand, and efficiency in various sectors, highlighting the decoupling of economic growth from resource consumption. The findings underscore the challenge of promoting resource efficiency through car-sharing, indicating sector-specific variations in the value-added per unit of resource use. This information can aid decision-makers in formulating strategies for adopting product-service systems to advance sustainable consumption and production.
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