Stockpiles are a crucial part of mine planning. However, they are often ignored in longterm planning due to the difficulty of correctly evaluating their impact in mine scheduling. This difficulty arises mainly because...
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ISBN:
(纸本)9780873354172
Stockpiles are a crucial part of mine planning. However, they are often ignored in longterm planning due to the difficulty of correctly evaluating their impact in mine scheduling. This difficulty arises mainly because materials of different grades are mixed in a stockpile, and the final grade of the material leaving the stockpile is a complex non-linear function of the material inside the stockpile. In practice, computational software uses different (usually linear) approximations for estimating this grade, but it is not clear how good these approximations are. In this paper, we discuss different optimization models to approximate the real impact of a stockpile on long-term mine planning. We discuss the properties of these models and compare the quality of the approximations computationally. We show that it is possible to obtain good upper and lower bounds on the resulting grade of the stockpile, and realistic and accurate estimations of the behavior of the stockpile. We also discuss how to extend these models to address different minerals and their corresponding grades.
Many recent planning heuristics are based on LP optimization. However, planning experts mostly use LP solvers as a black box and it is often not obvious to them which LP techniques would be most suitable for their spe...
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Let D, called the domain, be a fixed finite set and let Gamma, called the valued constraint language, be a fixed set of functions of the form f : D-m -> Q U {infinity}, where different functions might have differen...
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Let D, called the domain, be a fixed finite set and let Gamma, called the valued constraint language, be a fixed set of functions of the form f : D-m -> Q U {infinity}, where different functions might have different arity m. We study the valued constraint satisfaction problem parametrized by Gamma, denoted by VCSP(Gamma). These are minimization problems given by n variables and the objective function given by a sum of functions from Gamma, each depending on a subset of the n variables. For example, if D = {0, 1} and Gamma contains all ternary {0,infinity}-valued functions, VCSP(Gamma) corresponds to 3-SAT. More generally, if Gamma contains only {0,infinity}-valued functions, VCSP(Gamma) corresponds to CSP(Gamma). If D = {0, 1} and Gamma contains all ternary {0, 1}-valued functions, VCSP(Gamma) corresponds to Min-3-SAT, in which the goal is to minimize the number of unsatisfied clauses in a 3-CNF instance. Finite-valued constraint languages contain functions that take on only rational values and not infinite values. Our main result is a precise algebraic characterization of valued constraint languages whose instances can be solved exactly by the basic linear programming relaxation (BLP). For a valued constraint language Gamma, BLP is a decision procedure for Gamma if and only if Gamma admits a symmetric fractional polymorphism of every arity. For a finite-valued constraint language Gamma, BLP is a decision procedure if and only if Gamma admits a symmetric fractional polymorphism of some arity, or equivalently, if Gamma admits a symmetric fractional polymorphism of arity 2. Using these results, we obtain tractability of several novel classes of problems, including problems over valued constraint languages that are (1) submodular on arbitrary lattices;(2) k-submodular on arbitrary finite domains;(3) weakly (and hence strongly) tree submodular on arbitrary trees.
In this paper, a new unconstrained minimization problem formulation is proposed for linear programming twin support vector machine (TWSVM) classifiers. The proposed formulation leads to two smaller-sized unconstrained...
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In this paper, a new unconstrained minimization problem formulation is proposed for linear programming twin support vector machine (TWSVM) classifiers. The proposed formulation leads to two smaller-sized unconstrained minimization problems having their objective functions piecewise differentiable. However, since their objective functions contain the non-smooth "plus" function, two new smoothing approaches are assumed to solve the proposed formulation, and then apply Newton-Armijo algorithm. The idea of our formulation is to reformulate TWSVM as a strongly convex problem by incorporated regularization techniques and then derive smooth 1-norm linear programming formulation for TWSVM to improve robustness. One significant advantage of our proposed algorithm over TWSVM is that the structural risk minimization principle is implemented in the primal problems which embodies the marrow of statistical learning theory. In addition, the solution of two modified unconstrained minimization problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems in TWSVM and TBSVM, which leads to extremely simple and fast algorithm. Our approach has the advantage that a pair of matrix equation of order equals to the number of input examples is solved at each iteration of the algorithm. The algorithm converges from any starting point that can be easily implemented in MATLAB without using any optimization packages. The performance of our proposed method is verified experimentally on several benchmark and synthetic datasets. Experimental results show the effectiveness of our methods in both training time and classification accuracy.
This paper uses approximate linear programming (ALP) to compute average cost bounds for queueing network control problems. Like most approximate dynamic programming (ADP) methods, ALP approximates the differential cos...
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This paper uses approximate linear programming (ALP) to compute average cost bounds for queueing network control problems. Like most approximate dynamic programming (ADP) methods, ALP approximates the differential cost by a linear form. New types of approximating functions are identified that offer more accuracy than previous ALP studies or other performance bound methods. The structure of the infinite constraint set is exploited to reduce it to a more manageable set. When needed, constraint sampling and truncation methods are also developed. Numerical experiments show that the LPs using quadratic approximating functions can be easily solved on examples with up to 17 buffers. Using additional functions reduced the error to 1-5% at the cost of larger LPs. These ALPs were solved for systems with up to 6-11 buffers, depending on the functions used. The method computes bounds much faster than value iteration. It also gives some insights into policies. The ALPs do not scale to very large problems, but they offer more accurate bounds than other methods and the simplicity of just solving an LP. (C) 2015 Elsevier Ltd. All rights reserved.
This paper proposes a V2G charging scheduling scheme for energy invoice minimization of railway station parking lots hosting plug-in electric vehicles. Using a two layer optimization technique the daily load profile o...
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ISBN:
(纸本)9781467384643
This paper proposes a V2G charging scheduling scheme for energy invoice minimization of railway station parking lots hosting plug-in electric vehicles. Using a two layer optimization technique the daily load profile of the railway station is reduced in order to increase the load factor and minimizing the annual energy invoice (AEI) of the station. Binary linear programming is used as second layer for charging/discharging scheduling problem. The final results shows interests of using the proposed approach conducting its impact on minimizing annual optimum subscribed power, maximum demand power and annual energy invoice.
New technologies allow to carry out a change in the way we educate more focused on the student rather than the teacher. In this regard, with the support of Information and Communications Technology (TIC) is very commo...
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New technologies allow to carry out a change in the way we educate more focused on the student rather than the teacher. In this regard, with the support of Information and Communications Technology (TIC) is very common nowadays, students are increasingly using their mobile devices in the classroom, becoming the best friend student, figuratively. Based on the above drawing these guidelines, this paper presents the development of an App for calculating basic feasible solutions to transport problems to support the teaching-learning process to be used inside and outside the classroom; this app is to provide a learning environment movement to transport problems in linear programming (PL).
Adaptive linear programming (ALP) decoders are mainly used for decoding low-density parity-check (LDPC) codes. The principle of ALP decoders is based on generating redundant-parity check equations, which could elimina...
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Adaptive linear programming (ALP) decoders are mainly used for decoding low-density parity-check (LDPC) codes. The principle of ALP decoders is based on generating redundant-parity check equations, which could eliminate fractional solutions of linear programming (LP) decoders. These generated redundant parity check equations increase the error rate performance of decoder. In this paper, LP model is defined with auxiliary variables to facilitate finding redundant-parity check equations, and redundant parity check equations are searched over proposed LP model with a heuristic method. Simulation results demonstrate that the proposed algorithm could find redundant-parity check equations in a shorter time than other ALP decoders.
Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets of distributions. An algorithm for approximate credal network updating is presented. The problem in its general formulation i...
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Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets of distributions. An algorithm for approximate credal network updating is presented. The problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. This simple idea can be iterated and quickly leads to accurate inferences. A transformation is also derived to reduce decision making in credal networks based on the maximality criterion to updating. The decision task is proved to have the same complexity of standard inference, being NPPP-complete for general credal nets and NP-complete for polytrees. Similar results are derived for the E-admissibility criterion. Numerical experiments confirm a good performance of the method. (C) 2014 Elsevier Inc. All rights reserved.
This paper describes a linear programming (LP) approach for solving the network utility maximization problem. The developed approach is inspired by a convex relaxation technique from non-convex polynomial optimization...
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ISBN:
(纸本)9781509013364
This paper describes a linear programming (LP) approach for solving the network utility maximization problem. The developed approach is inspired by a convex relaxation technique from non-convex polynomial optimization methods. In contrast to most of the existing results where concavity of the network's utility function is often assumed, the proposed LP approach may still be used to solve the NUM problem even in the absence of such a concavity assumption. Although the presented LP approach is originally formulated to compute upper bounds for the global optima of the NUM problem, we illustrate through simulation examples that the obtained bounds often correspond to the exact global optima.
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