The problem of finding static-arbitrage bounds on basket option prices has received a growing attention in the literature. In this paper, we focus on the lower bound case and propose a novel efficient solution procedu...
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The problem of finding static-arbitrage bounds on basket option prices has received a growing attention in the literature. In this paper, we focus on the lower bound case and propose a novel efficient solution procedure that is based on the separation problem. The computational burden of the proposed method is polynomial in the input data size. We also discuss the case of possibly negative weight vectors which can be applied to spread options.
In the literature, sensitivity analysis of linear programming (LP) has been widely studied. However, only some very simple and special cases were considered when right-hand side (RHS) parameters or objective function ...
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In the literature, sensitivity analysis of linear programming (LP) has been widely studied. However, only some very simple and special cases were considered when right-hand side (RHS) parameters or objective function coefficients (OFC) correlate to each other. In the presence of correlation when one parameter changes, other parameters vary, too. Here principal component analysis is used to convert the correlation of the LP homogenous parameters into functional relations. Then, using the derivatives of the functional relations, it is possible to perform classical sensitivity analysis for the LP with correlation among RHS parameters or OFC. The validity of the devised method is corroborated by open literature examples having correlation among homogenous parameters.
The paper is concerned with linear programming problems whose input data may be intuitionistic fuzzy (IF) while the values of variables are always real numbers. We propose rather general approach to this type of probl...
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The paper is concerned with linear programming problems whose input data may be intuitionistic fuzzy (IF) while the values of variables are always real numbers. We propose rather general approach to this type of problems based on level sets, and present recent results for problems in which the notions of feasibility and optimality are based on the IF relations. Special attention is devoted to the weak and strong duality.
The usual formulation of a linear program is max . The core part of this linear program is the matrix since the columns define the variables and the rows define the constraints. The matrix is constructed by populating...
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The usual formulation of a linear program is max . The core part of this linear program is the matrix since the columns define the variables and the rows define the constraints. The matrix is constructed by populating columns or populating rows, or some of both, depending on the nature of the data and how it is collected. This paper addresses the construction of the matrix and solution procedures when there are separate data sources for the columns and for the rows and, moreover, the data is uncertain. The matrices which are "realizable" are only those which are considered possible from both sources. These realizable matrices then form an uncertainty set for a robust linear program. We show how to formulate and solve linear programs which provide lower and upper bounds to the robust linear program defined by . We also show how to use ordinary linear programming duality to share and divide the "credit/responsibility" of the optimal value of the robust linear program between the two alternative data sources.
Eating healthier or vegetarian and vegan diets are suggested options to reduce the environmental impact of the current diet. In this paper, we demonstrate a method that is able to identify diets with reduced environme...
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Eating healthier or vegetarian and vegan diets are suggested options to reduce the environmental impact of the current diet. In this paper, we demonstrate a method that is able to identify diets with reduced environmental impact and are more similar to the current diet than predetermined scenarios. All diets were adequate and consisted of commonly available foods. We used linear programming to find solutions that are as close as possible to the current diet of an average woman with age 31-50, first without any food groups' constraints and later by imposing constraints on meat, fish, dairy, and eggs. Finally, we use a similar technique to search for the closest diet that achieves the same environmental reduction as the most restricted option (no meat, fish, dairy, or eggs), without restrictions on products. In the optimization algorithm, we incorporate popularity of food products in order to design menus which are feasible for the Dutch population. The results show that simply eating according to guidelines does not guarantee a diet with an improved environmental profile. Removing meat and fish from the diet reduces the environmental impact by about 21 %. A healthy vegan diet reaches 30 % environmental impact reduction, but leads to a diet with many changes in comparison to a typical Dutch diet and without meeting one of the health constraints (EPA + DHA-Eicosapentaenoic acid + Docosahexaenoic acid). We show that it is possible to find less restrictive solutions than vegetarian or vegan diets that still satisfy all nutritional requirements and have less environmental impact than the current one. Just eating healthier is not enough in order to reduce environmental impact. However, designing a diet that meets dietary requirements must be a prerequisite for sustainable diets. Simply removing products from a diet can have as consequence that other products have to be added to compensate for the nutritional imbalances. We show, by using linear programming, that it is poss
Interval fuzzy preference relations that can well cope with the vagueness and uncertainty are commonly used by the decision maker. The most crucial issue is how to derive the interval priority vector from an interval ...
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Interval fuzzy preference relations that can well cope with the vagueness and uncertainty are commonly used by the decision maker. The most crucial issue is how to derive the interval priority vector from an interval fuzzy preference relation. This paper first analyzes the size of the interval priority weights. Then, two linear programming models are built, by which the interval priority weights are obtained, respectively. Considering the inconsistent case, two consistency-based linear programming models are built to derive the additive consistent fuzzy preference relations. Different to the current methods, new models consider the consistency and the interval priority weight simultaneously. In some situations, the decision maker may only offer an incomplete interval fuzzy preference relation, namely, some judgments are missing. To cope with this situation, we first classify the missing intervals into three categories and then apply the associated linear equations to denote the missing values. After that, we construct two consistency-based linear programming models to determine the missing values to cope with the consistent and inconsistent cases. It is worth noting that the built models can cope with the situation where ignorance objects exist. Meanwhile, the associated numerical examples are offered, and the analysis comparison is made. (C) 2016 Elsevier B.V. All rights reserved.
Due to the circuit aging effect, the minimum leakage vector (MLV) found by the traditional input vector control method may not obtain the optimal leakage power reduction result when the circuit begins to degrade. To s...
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Due to the circuit aging effect, the minimum leakage vector (MLV) found by the traditional input vector control method may not obtain the optimal leakage power reduction result when the circuit begins to degrade. To solve this problem, we present an adaptive MLV selection strategy based on a linear programming approach. The method divides the total lifetime of the circuit into a succession of time intervals, and the MLV used in each interval is periodically updated according to the transistor's threshold voltage degradation so that the best overall power reduction result can be achieved. Experimental results on various benchmark circuits show the effectiveness of our method. (C) 2016 Elsevier B.V. All rights reserved.
In this paper we propose a novel two-step linear optimization model to calculate energy efficient timetables in metro railway networks. The resultant timetable minimizes the total energy consumed by all trains and max...
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In this paper we propose a novel two-step linear optimization model to calculate energy efficient timetables in metro railway networks. The resultant timetable minimizes the total energy consumed by all trains and maximizes the utilization of regenerative energy produced by braking trains, subject to the constraints in the railway network. In contrast to other existing models, which are NP-hard, our model is computationally the most tractable one being a linear program. We apply our optimization model to different instances of service PES2-SFM2 of line 8 of Shanghai Metro network spanning a full service period of one day (18 h) with thousands of active trains. For every instance, our model finds an optimal timetable very quickly (largest runtime being less than 13 s) with significant reduction in effective energy consumption (the worst case being 19.27%). Code based on the model has been integrated with Thales Timetable Compiler - the industrial timetable compiler of Thales Inc that has the largest installed base of communication based train control systems worldwide. (C) 2016 Elsevier Ltd. All rights reserved.
The goal of the research is to propose an optimization-based methodology for the evaluation of retrofit incentives, using as a benchmark the wide data collection reported by the ENEA Italian Agency since 2007. To dete...
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The goal of the research is to propose an optimization-based methodology for the evaluation of retrofit incentives, using as a benchmark the wide data collection reported by the ENEA Italian Agency since 2007. To determine the best mix of energy retrofit measures for different areas of Italy, two linear programming models are proposed. The first model maximizes energy savings and the second one minimizes retrofit costs. The results show a 17% reduction in the average cost for each MW h of saved energy. More importantly, the methodology can help decision-makers appreciate how energy efficiency incentives have been used so far and how effective they could be. Furthermore, the methodology can be used for setting future incentive distribution plans. (C) 2015 Elsevier B.V. All rights reserved.
Quantized massive multiple-input-multiple-output (MIMO) systems are gaining more interest due to their power efficiency. We present a new precoding technique to mitigate the multi-user interference and the quantizatio...
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ISBN:
(纸本)9781509030095
Quantized massive multiple-input-multiple-output (MIMO) systems are gaining more interest due to their power efficiency. We present a new precoding technique to mitigate the multi-user interference and the quantization distortions in a downlink multi-user (MU) multiple-input-single-output (MISO) system with 1-bit quantization at the transmitter. This work is restricted to PSK modulation schemes. The transmit signal vector is optimized for every desired received vector taking into account the 1-bit quantization. The optimization is based on maximizing the safety margin to the decision thresholds of the PSK modulation. Simulation results show a significant gain in terms of the uncoded bit-error-ratio (BER) compared to the existing linear precoding techniques.
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