In this paper, we propose an arc-search interior-point algorithm for convex quadratic programming with a wide neighborhood of the central path, which searches the optimizers along the ellipses that approximate the ent...
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In this paper, we propose an arc-search interior-point algorithm for convex quadratic programming with a wide neighborhood of the central path, which searches the optimizers along the ellipses that approximate the entire central path. The favorable polynomial complexity bound of the algorithm is obtained, namely O（nlog（（ x^0）~TS^0/ε）） which is as good as the linear programming analogue. Finally, the numerical experiments show that the proposed algorithm is efficient.
The paper presents, An optimal Static State-Feedback controller (SSF) approach, addressed to class of MultiInput/Multi-Output (MIMO) Linear Invariant-Time (LTI) systems. The block roots of the matrix polynomial are th...
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ISBN:
(纸本)9781538668665
The paper presents, An optimal Static State-Feedback controller (SSF) approach, addressed to class of MultiInput/Multi-Output (MIMO) Linear Invariant-Time (LTI) systems. The block roots of the matrix polynomial are the main key in the design of the proposed SSF controller, an optimal set of diagonally block roots have been chosen with constraints conditions, where the SSF matrix gain controller K is formulated as a nonlinear convex optimization problem, and it is solved using the interior-point algorithm (IPA), the optimal block roots are assigned based on the canonical controller block transformations and the right block Vandermonde matrix respectively. To show the performances of the proposed SSF controller, a centrifugal gas compressor system is introduced as case study. Based on this application, a comparative study is carried out, with two well known controllers: SSF based Pole Placement (PP) and SSF based Linear Quadratic Regulator (LQR), aims to demonstrate the feasibility of our proposed design methodology.
In this paper we present an infeasible primal-dual interior-point algorithm for linearly constrained convex optimization based on a parametric kernel function, with parameters p is an element of [0, 1] and q >= 1. ...
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ISBN:
(纸本)9780769536057
In this paper we present an infeasible primal-dual interior-point algorithm for linearly constrained convex optimization based on a parametric kernel function, with parameters p is an element of [0, 1] and q >= 1. Numerical test shows that the efficiency of the proposed algorithm and investigates the behavior of the algorithm with different parameters p, q and theta.
The purpose of this paper is to propose a recursive maximum likelihood method based on interior-point algorithm to online estimate the uncertain aerodynamic parameters for hypersonic vehicles. In order to improve the ...
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ISBN:
(纸本)9781467355339
The purpose of this paper is to propose a recursive maximum likelihood method based on interior-point algorithm to online estimate the uncertain aerodynamic parameters for hypersonic vehicles. In order to improve the identification performance, boundaries of unknown parameters are introduced as prior knowledge to covert the online estimation problem into a constrained optimization problem. Then the interior-point algorithm is applied to solve this problem. This algorithm can improve the accuracy and efficiency of typical maximum likelihood estimation, reduce it’s dependence on initial conditions and always keep the identification result in a reasonable range. Simulation results demonstrate the effectiveness of the method.
We describe an infeasible-interior-point algorithm for monotone linear complementarity problems that has polynomial complexity, global linear convergence, and local superlinear convergence with a Q-order of 2. Only on...
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We describe an infeasible-interior-point algorithm for monotone linear complementarity problems that has polynomial complexity, global linear convergence, and local superlinear convergence with a Q-order of 2. Only one matrix factorization is required per iteration, and the analysis assumes only that a strictly complementary solution exists.
In this paper, we design and analyze a new full-step interior-point algorithm for linear complementarity problem based on a very simple function. The algorithm uses the simple function to determine the searching direc...
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In this paper, we design and analyze a new full-step interior-point algorithm for linear complementarity problem based on a very simple function. The algorithm uses the simple function to determine the searching direction and define the neighborhood of central path. The full-step used in the algorithm has local quadratic convergence property according to the proximity function which is also constructed by this simple function. We derive the iteration complexity for the algorithm and obtain the best-known iteration bounds for linear complementarity problem.
In this paper, ellipsoidal estimations are used to track the central path of a linear complementarity problem (LCP). A wide neighborhood primal-dual interior-point algorithm is devised to search an epsilon-approximate...
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In this paper, ellipsoidal estimations are used to track the central path of a linear complementarity problem (LCP). A wide neighborhood primal-dual interior-point algorithm is devised to search an epsilon-approximate solution of the LCP along the ellipse. The algorithm is proved to be polynomial with the complexity bound O (nlog (x(0))(T)s(0)/epsilon), which is as good as the linear programming analogue. The numerical results show that the proposed algorithm is efficient and reliable.
interior-point methods （IPMs） for linear optimization （LO） and semidefinite optimization （SDO） have become a hot area in mathematical programming in the last decades. In this paper, a new kernel function with si...
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interior-point methods （IPMs） for linear optimization （LO） and semidefinite optimization （SDO） have become a hot area in mathematical programming in the last decades. In this paper, a new kernel function with simple algebraic expression is proposed. Based on this kernel function, a primal-dual interior-point methods （IPMs） for semidefinite optimization （SDO） is designed. And the iteration complexity of the algorithm as O（n^3/4 log n/ε） with large-updates is established. The resulting bound is better than the classical kernel function, with its iteration complexity O（n log n/ε） in large-updates case.
In this paper, a primal-dual path-following interior-point algorithm for linearly constrained convex optimization(LCCO) is *** algorithm is based on a new technique for finding a class of search directions and the str...
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In this paper, a primal-dual path-following interior-point algorithm for linearly constrained convex optimization(LCCO) is *** algorithm is based on a new technique for finding a class of search directions and the strategy of the central *** each iteration, only full-Newton steps are ***, the favorable polynomial complexity bound for the algorithm with the small-update method is deserved, namely, O(√n log n /ε).
In this paper, an efficient primal-dual interiorpointalgorithm for large-update methods is introduced by means of a new kernel function. We analysis the complexity of the algorithm and conclude that its iteration ...
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In this paper, an efficient primal-dual interiorpointalgorithm for large-update methods is introduced by means of a new kernel function. We analysis the complexity of the algorithm and conclude that its iteration bounds, O(n3/4 logn/ε), is so far the best complexity result for large-update primal-dual interior-point methods.
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