Mathematical modeling structure was developed to support representative Brazilian bulb growing and trading company's decision making process, during the Gladiolus production planning activity. The pertinent LP mod...
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We show that any linear program (LP) in n nonnegative variables and m equality constraints defines in a natural way a unique sink orientation of the n-dimensional cube. From the sink of the cube, we can either read of...
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ISBN:
(纸本)9780898716054
We show that any linear program (LP) in n nonnegative variables and m equality constraints defines in a natural way a unique sink orientation of the n-dimensional cube. From the sink of the cube, we can either read off an optimal solution to the LP, or we obtain certificates for infeasibility or unboundedness. This reduction complements the implicit local neighborhoods induced by the vertex-edge structure of the feasible region with an explicit neighborhood structure that allows random access to all 2(n) candidate solutions. Using the currently best sink-finding algorithm for general unique sink orientations, we obtain the fastest deterministic LP algorithm in the RAM model, for the central case n = 2m.
We present an linear programming formulation of MDPs with countable state and action spaces and no unichain assumption. This is an extension of the Hordijk and Kallenberg (1979) formulation in finite state and action ...
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Guides in the application of linear programming to firm decision making, with the goal of giving decision-makers a better understanding of methods at their disposal Useful as a main resource or as a supplement in an e...
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ISBN:
(数字)9781119509455
ISBN:
(纸本)9781119509448
Guides in the application of linear programming to firm decision making, with the goal of giving decision-makers a better understanding of methods at their disposal Useful as a main resource or as a supplement in an economics or management science course, this comprehensive book addresses the deficiencies of other texts when it comes to covering linear programming theory--especially where data envelopment analysis (DEA) is concerned--and provides the foundation for the development of DEA. linear programming and Resource Allocation Modeling begins by introducing primal and dual problems via an optimum product mix problem, and reviews the rudiments of vector and matrix operations. It then goes on to cover: the canonical and standard forms of a linear programming problem; the computational aspects of linear programming; variations of the standard simplex theme; duality theory; single- and multiple- process production functions; sensitivity analysis of the optimal solution; structural changes; and parametric programming. The primal and dual problems are then reformulated and re-examined in the context of Lagrangian saddle points, and a host of duality and complementary slackness theorems are offered. The book also covers primal and dual quadratic programs, the complementary pivot method, primal and dual linear fractional functional programs, and (matrix) game theory solutions via linear programming, and data envelopment analysis (DEA). This book: Appeals to those wishing to solve linear optimization problems in areas such as economics, business administration and management, agriculture and energy, strategic planning, public decision making, and health care Fills the need for a linear programming applications component in a management science or economics course Provides a complete treatment of linear programming as applied to activity selection and usage Contains many detailed example problems as well as textual and graphical explanations linear programming and Resourc
It is well recognized that support vector machines (SVMs) would produce better classification performance in terms of generalization power. A SVM constructs an optimal separating hyper-plane through maximizing the mar...
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ISBN:
(纸本)1424400600
It is well recognized that support vector machines (SVMs) would produce better classification performance in terms of generalization power. A SVM constructs an optimal separating hyper-plane through maximizing the margin between two classes in high-dimensional feature space. The inverse problem is how to split a given dataset into two clusters such that the margin between the two clusters attains the maximum. It is difficult to give an exact solution to this problem, so a genetic algorithm is designed to solve this problem. But the proposed genetic algorithm has large time complexity for the process of solving quadratic programs. In this paper, we replace the quadratic programming with a linear programming. The new algorithm can greatly decrease time complexity. The fast algorithm for acquiring the maximum margin can upgrade the applicability of the proposed genetic algorithm.
In this work(1), we present a new multiple-input-multiple-output (MIMO) receiver that integrates the MIMO signal detection and the decoding of low density parity check coded data. This joint MIMO detector and decoder ...
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ISBN:
(纸本)9781467309219;9781467309202
In this work(1), we present a new multiple-input-multiple-output (MIMO) receiver that integrates the MIMO signal detection and the decoding of low density parity check coded data. This joint MIMO detector and decoder utilizes linear programming and achieves about 9.0 dB gain over existing works in terms of bit error rate (BER) of 4 x 10(-5) with comparable computational complexity. The proposed detector also outperforms the classic turbo equalizer by achieving up to 4.0 dB improvement over turbo equalizer at frame error rate (FER) of 1 x 10(4). In fact, we can achieve further gain by improving the proposed joint detector through the use of redundant parity checks.
The problem of synthesizing stabilizing state-feedback controllers is solved when the closed-loop system is required to remain positive, for the class of 2-D linear systems described by the Fornasini-Marchesini second...
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ISBN:
(纸本)9783642028939
The problem of synthesizing stabilizing state-feedback controllers is solved when the closed-loop system is required to remain positive, for the class of 2-D linear systems described by the Fornasini-Marchesini second model. First, a constructive necessary and sufficient condition expressed as a linear programming problem is provided for stabilization of these systems when the states must be nonnegative (assuming that the boundary conditions are nonnegative). It is shown how it is simple to include additional constraints (such as positive controls). Moreover, this result is also extended to include uncertainty in the model, making possible to synthesize robust state-feedback controllers, solving linear programming problems. Some numerical examples are included to illustrate the proposed approach for different design problems.
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.
By providing substantial gains in terms of both spectral and energy-efficiency, Massive MIMO is expected to be the promising enabler for the fifth generation (5G) communications. However the performance of massive MIM...
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ISBN:
(纸本)9781509016983
By providing substantial gains in terms of both spectral and energy-efficiency, Massive MIMO is expected to be the promising enabler for the fifth generation (5G) communications. However the performance of massive MIMO is greatly affected by pilot contamination due to the insufficiency of pilot sequences. To overcome this, we propose a linear programming based pilot allocation with the purpose of alleviating the effect of pilot contamination and maximizing the system throughput. We first formulate the pilot allocation as a user clustering problem, which can be converted to a linear programming one by introducing the integer factor and constraint relaxation. An efficient linear programming algorithm is proposed to solve the problem. Simulation results demonstrate that the proposed scheme outperforms the other candidates in the presence of pilot contamination.
linear programming is a mathematical optimization technique used in numerous fields including mathematics, economics, and computer science, with numerous industrial contexts, including solving optimization problems su...
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ISBN:
(纸本)9781665445924
linear programming is a mathematical optimization technique used in numerous fields including mathematics, economics, and computer science, with numerous industrial contexts, including solving optimization problems such as planning routes, allocating resources, and creating schedules. As a result of its wide breadth of applications, a considerable amount of its user base is lacking in terms of programming knowledge and experience and thus often resorts to using graphical software such as Microsoft Excel. However, despite its popularity amongst less technical users, the methodologies used by these tools are often ad-hoc and prone to errors. To counteract this problem we propose creating a block-based language that allows users to create linear programming models using data contained inside spreadsheets. This language will guide the users to write syntactically and semantically correct programs and thus aid them in a way that current languages do not.
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