A linear programming Monte Carlo method (LPMC) is proposed to achieve the optimal restoration of the whole degraded coastal wetland ecosystem, i.e. the minimum restoration cost, in the case of uncertain ecological res...
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A linear programming Monte Carlo method (LPMC) is proposed to achieve the optimal restoration of the whole degraded coastal wetland ecosystem, i.e. the minimum restoration cost, in the case of uncertain ecological restoration costs of different degraded coastal wetlands. LPMC can comprehensively and systematically complete the dynamic analysis of coastal wetland ecosystem restoration cost, including the uncertainty analysis of ecological restoration cost and the screening of equivalent robust solutions. The applicability of this method is demonstrated by solving a practical problem of ecological restoration of coastal wetlands. The results show that this method can generate the robust optimal solution block for the globally optimal objective function and decision variables under the condition of restoring cost uncertainty, including a variety of optimal solutions adapted to the different needs.
The Lovasz theta number is a semidefinite programming bound on the clique number of (the complement of) a given graph. Given a vertex-transitive graph, every vertex belongs to a maximal clique, and so one can instead ...
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ISBN:
(数字)9781510629707
ISBN:
(纸本)9781510629707
The Lovasz theta number is a semidefinite programming bound on the clique number of (the complement of) a given graph. Given a vertex-transitive graph, every vertex belongs to a maximal clique, and so one can instead apply this semidefinite programming bound to the local graph. In the case of the Paley graph, the local graph is circulant, and so this bound reduces to a linear programming bound, allowing for fast computations. Impressively, the value of this program with Schrijver's nonnegativity constraint rivals the state-of-the-art closed-form bound recently proved by Hanson and Petridis. We conjecture that this linear programming bound improves on the Hanson-Petridis bound infinitely often, and we derive the dual program to facilitate proving this conjecture.
linear programming is a foundational tool for many aspects of integer and combinatorial optimization. This work studies the complexity of solving linear programs exactly over the rational numbers through use of an ora...
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ISBN:
(纸本)9783030179533;9783030179526
linear programming is a foundational tool for many aspects of integer and combinatorial optimization. This work studies the complexity of solving linear programs exactly over the rational numbers through use of an oracle capable of returning limited-precision LP solutions. Under mild assumptions, it is shown that a polynomial number of calls to such an oracle and a polynomial number of bit operations, is sufficient to compute an exact solution to an LP. Previous work has often considered oracles that provide solutions of an arbitrary specified precision. While this leads to polynomial-time algorithms, the level of precision required is often unrealistic for practical computation. In contrast, our work provides a foundation for understanding and analyzing the behavior of the methods that are currently most effective in practice for solving LPs exactly.
The covid-19 pandemic has severely affected the economy of the Philippines. With 90% of the labor force being affected, hundreds of thousands of families turn to their respective local government units for assistance....
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ISBN:
(纸本)9781665401685
The covid-19 pandemic has severely affected the economy of the Philippines. With 90% of the labor force being affected, hundreds of thousands of families turn to their respective local government units for assistance. LGUs have begun distributing food box assistance to every family under the Food Security Program to ease the economic burden. However, such a program having a vast number of recipients will require a large budget. This study presents the optimization of the content of the food packs used for food aid distribution through linear programming using Matlab. The study's goal is to maximize the nutritional content of the food pack while being under the constraint of a limited budget to ensure the best utilization of scarce resources.
The Hirsch Conjecture stated that any d-dimensional polytope with n facets has a diameter at most equal to n - d. This conjecture was disproven by Santos (A counterexample to the Hirsch Conjecture, Annals of Mathemati...
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linear programming (LP) problems aim to find the optimal solution to an objective under constraints. These problems typically require domain knowledge, mathematical skills, and programming ability, presenting signific...
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The standard minimum dispersion distortionless response (MDDR) beamformer can provide a performance improvement for non-Gaussian signals compared to the minimum variance distortionless response (MVDR) beamformer. Howe...
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ISBN:
(纸本)9781665468893
The standard minimum dispersion distortionless response (MDDR) beamformer can provide a performance improvement for non-Gaussian signals compared to the minimum variance distortionless response (MVDR) beamformer. However, the variety of the direction of the interference may have a great influence on the performance of the MDDR beamformer. To solve the problem, a robust beamformer with a broad null by linear programming is proposed for sub-Gaussian signals. Unlike the beamformers based on the minimum variance criterion, the proposed beamformer minimizes the $\ell_{p}$ -norm of the output and meanwhile constrains not only the target response to be unity, but also the average power of the interference's dynamic angular sector to zero. The resulting optimization problem is cast as a linear programming and hence can be solved efficiently. Simulation results indicate that the proposed method offers a significant performance improvement if the direction of the interference varies with time.
We present PDLP, a practical first-order method for linear programming (LP) that can solve to the high levels of accuracy that are expected in traditional LP applications. In addition, it can scale to very large probl...
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We focus on multi-agent reinforcement learning in tabular average-cost settings: a team of agents sequentially interacts with the environment and observes localized incentives. The setting we focus on is one in which ...
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We focus on multi-agent reinforcement learning in tabular average-cost settings: a team of agents sequentially interacts with the environment and observes localized incentives. The setting we focus on is one in which the global reward is a sum of all local rewards, the joint policy factorizes into agents' marginals, and full observability. To date, exceptionally few global optimality guarantees exist for this simple setting, as most results, asymptotic or non-asymptotic, yield convergence to stationarity under parameterized settings for possibly large/continuous spaces. To strengthen performance guarantees in MARL, we focus on linear programming (LP) reformulations of RL for which stochastic primal-dual method has recently been shown to achieve optimal sample complexity in the centralized tabular case. We develop multi-agent LP extensions, whereby agents solve their local saddle point problems and then compose their variable estimates with weighted averaging steps to diffuse information between agents across time. We establish that the number of samples required to attain near-globally optimal solutions matches tight dependencies on the cardinality of the state and action spaces, and exhibits classical scalings with the size of the team in accordance with multi-agent optimization. Experiments then demonstrate the merits of this approach for cooperative navigation problems.
Picture improvement has been discovered to be perhaps the main vision applications since it can upgrade the computerized pictures with the goal that the outcomes are more appropriate for show or further picture examin...
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ISBN:
(纸本)9781665436571
Picture improvement has been discovered to be perhaps the main vision applications since it can upgrade the computerized pictures with the goal that the outcomes are more appropriate for show or further picture examination. To work on the nature of computerized pictures there are remarkable strategies that have been proposed. The goal is to manage picture handling and its major strides after that we had zeroed in on the diverse picture upgrade procedures. Since picture clearness is effectively influenced by lighting, climate, or gear that has been utilized to catch the picture. These conditions lead to loss of data. The principle motivation behind picture improvement is to bring out detail that is covered up in a picture or to expand contrast during a low difference picture. It gives countless decisions for working on the visual nature of pictures. Its article is to dissect the specific picture attributes for examination, end and further use.
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