In this paper, we show that the average number of steps of the Lemke algorithm for the quadratic programming problems grows at most linearly in the number of variables while fixing the number of constraints. The resul...
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In this paper, we show that the average number of steps of the Lemke algorithm for the quadratic programming problems grows at most linearly in the number of variables while fixing the number of constraints. The result and method were motivated by Smale's result on linear programmingproblems [cf. 4]. We also give the probability that a quadratic programming problem indeed possesses a finite optimal solution.
Isometric correspondence is an important technique for surface correspondence. Recently, numerous algorithms have been proposed to build isometric mapping. However, those methods tend to be error prone due to the topo...
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Isometric correspondence is an important technique for surface correspondence. Recently, numerous algorithms have been proposed to build isometric mapping. However, those methods tend to be error prone due to the topological variation and noises of the dynamic surface. To address this issue, we propose a dynamic surface correspondence method by computing maximum spatial-temporal isometric cluster. Firstly, the algorithm defines a maximum isometric cluster score to measure the correspondence quality of each cluster in the product space. Then, the maximum problem is formulated into a quadratic programming problem. Furthermore, we define a similarity function which explicitly encodes the spatial-temporal consistence of the dynamic surface. It can greatly reduce the solving dimension, and improve the correspondence accuracy. Finally, the result is extended to the dense correspondence by a geodesic distance vector. Experimental results show that our algorithm can generate consistent correspondence on three databases of surface sequences, which outperforms existing state-of-the-art algorithms.
A nonconvex generalized semi-infinite programmingproblem is considered, involving parametric max-functions in both the objective and the constraints. For a fixed vector of parameters, the values of these parametric m...
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A nonconvex generalized semi-infinite programmingproblem is considered, involving parametric max-functions in both the objective and the constraints. For a fixed vector of parameters, the values of these parametric max-functions are given as optimal values of convex quadratic programming problems. Assuming that for each parameter the parametric quadraticproblems satisfy the strong duality relation, conditions are described ensuring the uniform boundedness of the optimal sets of the dual problems w.r.t. the parameter. Finally a branch-and-bound approach is suggested transforming the problem of finding an approximate global minimum of the original nonconvex optimization problem into the solution of a finite number of convex problems.
This paper presents a new neural network model for solving degenerate quadratic minimax (DQM) problems. On the basis of the saddle point theorem, optimization theory, convex analysis theory, Lyapunov stability theory ...
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This paper presents a new neural network model for solving degenerate quadratic minimax (DQM) problems. On the basis of the saddle point theorem, optimization theory, convex analysis theory, Lyapunov stability theory and LaSalle invariance principle, the equilibrium point of the proposed network is proved to be equivalent to the optimal solution of the DQM problems. It is also shown that the proposed network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the original problem. Several illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper. (C) 2011 Elsevier B.V. All rights reserved.
Support vector machine has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. Training a support vector machine requires the solution of a very la...
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Support vector machine has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. Training a support vector machine requires the solution of a very large quadratic programming problem. Traditional optimization methods cannot be directly applied due to memory restrictions. Up to now, several approaches exist for circumventing the above shortcomings and work well. Another learning algorithm, particle swarm optimization, for training SVM is introduted. The method is tested on UCI datasets.
This paper derives an exact asymptotic expression for P-xu {there exists X-t(>= 0)(t) - mu t is an element of u}, as u -> infinity, where X(t) = (X-1(t),..., X-d(t))(T), t >= 0 is a correlated d-dimensional B...
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This paper derives an exact asymptotic expression for P-xu {there exists X-t(>= 0)(t) - mu t is an element of u}, as u -> infinity, where X(t) = (X-1(t),..., X-d(t))(T), t >= 0 is a correlated d-dimensional Brownian motion starting at the point x(u) = -alpha u with alpha is an element of R-d, mu is an element of R-d and U = Pi(d)(i)(=1) [0, infinity). The derived asymptotics depends on the solution of an underlying multidimensional quadratic optimization problem with constraints, which leads in some cases to dimension-reduction of the considered problem. Complementary, we study asymptotic distribution of the conditional first passage time to U, which depends on the dimension-reduction phenomena. (C) 2018 Elsevier B.V. All rights reserved.
Unmanned aerial vehicles (UAVs) can be deployed to combine terrestrial and aerial networks to provide flexible connectivity to a large number of devices in three-dimensional (3D) space. Powering wireless devices throu...
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Unmanned aerial vehicles (UAVs) can be deployed to combine terrestrial and aerial networks to provide flexible connectivity to a large number of devices in three-dimensional (3D) space. Powering wireless devices through energy harvesting and wireless power transfer have been investigated to provide uninterrupted operations. Resource allocation is critical to improving the overall performance of the network. This paper focusses on a downlink network in which UAVs serve as base stations to provide connectivity to ground users. A priority-based charging of UAVs from the ground charging stations needs to be efficiently designed for the sustainable operation of the overall network. A binary linear integer programming (BLIP) problem is formulated to minimise the charging cost and maximise the number of UAVs that can be charged by charging stations. First, the BLIP is transformed into a quadratic programming problem to solve it in polynomial time. A sequential quadraticprogramming algorithm is developed to solve the optimisation problem. Simulation results demonstrate the effectiveness of the proposed work compared to existing solutions.
Let X(t), t. R, be a d-dimensional vector-valued Brownian motion, d = 1. For all b. Rd \ (-8, 0] d we derive exact asymptotics of P{X(t + s) - X(t) > ub for some t. [0, T], s. [0, 1]} as u -> infinity, that is t...
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Let X(t), t. R, be a d-dimensional vector-valued Brownian motion, d = 1. For all b. Rd \ (-8, 0] d we derive exact asymptotics of P{X(t + s) - X(t) > ub for some t. [0, T], s. [0, 1]} as u -> infinity, that is the asymptotical behavior of tail distribution of vector-valued analog of Sheppstatistics for X;we cover not only the case of a fixed time-horizonT > 0 but also cases where T. 0 or T. 8. Results for high level excursion probabilities of vector-valued processes are rare in the literature, with currently no available approach suitable for our problem. Our proof exploits some distributional properties of vectorvalued Brownian motion, and results from quadratic programming problems. As a by-product we derive a new inequality for the `supremum' of vector-valued Brownian motions.
In this paper, some global optimality conditions for nonconvex minimization problems subject to quadratic inequality constraints are presented. Then some sufficient and necessary global optimality conditions for nonli...
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In this paper, some global optimality conditions for nonconvex minimization problems subject to quadratic inequality constraints are presented. Then some sufficient and necessary global optimality conditions for nonlinear programmingproblems with box constraints are derived. We also establish a sufficient global optimality condition for a nonconvex quadratic minimization problem with box constraints, which is expressed in a simple way in terms of the problem's data. In addition, a sufficient and necessary global optimality condition for a class of nonconvex quadratic programming problems with box constraints is discussed. We also present some numerical examples to illustrate the significance of our optimality conditions.
This paper characterizes the continuity property of the optimal value function in a general parametric quadratic programming problem with linear constraints. The lower semicontinuity and upper semicontinuity propertie...
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This paper characterizes the continuity property of the optimal value function in a general parametric quadratic programming problem with linear constraints. The lower semicontinuity and upper semicontinuity properties of the optimal value function are studied as well.
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