This paper is concerned with the computation of perfect stationary point, which is a strict refinement of stationary point. A differentiable homotopy method is developed for finding perfect stationary points of contin...
详细信息
This paper is concerned with the computation of perfect stationary point, which is a strict refinement of stationary point. A differentiable homotopy method is developed for finding perfect stationary points of continuous functions on convex polytopes. We constitute an artificial problem by introducing a continuously differentiable function of an extra variable. With the optimality conditions of this problem and a fixed point argument, a differentiable homotopy mapping is constructed. As the extra variable becomes close to zero, the homotopy path naturally provides a sequence of closely approximate stationary points on perturbed polytopes, and converges to a perfect stationary point on the original polytope. Numerical experiments are implemented to further illustrate the effectiveness of our method.
We consider a primal-scaling path-following algorithm for solving a certain class of monotone variational inequality problems. Included in this class are the convex separable programs considered by Monteiro and Adler ...
详细信息
We consider a primal-scaling path-following algorithm for solving a certain class of monotone variational inequality problems. Included in this class are the convex separable programs considered by Monteiro and Adler and the monotone linear complementarity problem. This algorithm can start from any interior solution and attain a global linear rate of convergence with a convergence ratio of 1 - c/square-root m, where m denotes the dimension of the problem and c is a certain constant. One can also introduce a line search strategy to accelerate the convergence of this algorithm.
Although the traditional phase-coding method for absolute phase retrieval possesses strong robustness, the method leads to unwrapping artifacts as a mass of codewords required. To solve this problem, a novel improved ...
详细信息
Although the traditional phase-coding method for absolute phase retrieval possesses strong robustness, the method leads to unwrapping artifacts as a mass of codewords required. To solve this problem, a novel improved method is proposed in this paper. The incorrect points in the recovered phase via the traditional method are located using a computational framework, and the path-following algorithm is adopted as an aid to correct the unwrapping artifacts. The effectiveness of the proposed method is experimentally verified by an established measurement system. The reconstructed geometry of standard sphere has sufficient accuracy and the absolute phase retrieval of a complex star object has high quality.
In this article, we propose a new second-order infeasible primal-dual path-following algorithm for symmetric cone optimization. The algorithm further improves the complexity bound of a wide infeasible primal-dual path...
详细信息
In this article, we propose a new second-order infeasible primal-dual path-following algorithm for symmetric cone optimization. The algorithm further improves the complexity bound of a wide infeasible primal-dual path-following algorithm. The theory of Euclidean Jordan algebras is used to carry out our analysis. The convergence is shown for a commutative class of search directions. In particular, the complexity bound is ?(r(5/4)logE(-1)) for the Nesterov-Todd direction, and ?(r(7/4)logE(-1)) for the xs and sx directions, where r is the rank of the associated Euclidean Jordan algebra and E is the required precision. If the starting point is strictly feasible, then the corresponding bounds can be reduced by a factor of r(3/4). Some preliminary numerical results are provided as well.
We describe a new potential function and a sequence of ellipsoids in the path-following algorithm for convex quadratic programming. Each ellipsoid in the sequence contains all of the optimal primal and dual slack vect...
详细信息
We describe a new potential function and a sequence of ellipsoids in the path-following algorithm for convex quadratic programming. Each ellipsoid in the sequence contains all of the optimal primal and dual slack vectors. Furthermore, the volumes of the ellipsoids shrink at the ratio $2^{ - \Omega (\sqrt n )} $ , in comparison to 2?Ω(1) in Karmarkar's algorithm and 2?Ω(1/n) in the ellipsoid method. We also show how to use these ellipsoids to identify the optimal basis in the course of the algorithm for linear programming.
Most existing path-following algorithms (PFAs) are developed for single-unit vehicles (SUVs) and rarely for articulated vehicles (AVs). Since these PFAs ignore the motion of the trailer, they may cause large tracking ...
详细信息
Most existing path-following algorithms (PFAs) are developed for single-unit vehicles (SUVs) and rarely for articulated vehicles (AVs). Since these PFAs ignore the motion of the trailer, they may cause large tracking deviations and ride stability issues when cornering. To this end, an Adaptive Articulation Angle Preview-based path-following algorithm (AAAP-PFA) is proposed for AVs. Different from previous PFAs, in this model, a simple linear vehicle dynamics model is used as the prediction model, and an offset distance calculated by an articulation angle is used as part of the preview distance. An adaptive posture control strategy is designed to trade off the trajectory tracking performance and lateral stability performance during the path-following process. Considering a large prediction mismatch caused by using a linear vehicle dynamics model, a feedback correction method is proposed to improve the robustness of the steering control. In the comparison simulation experiment with SUV-PFA, it is confirmed that the novel PFA has better adaptability to the contradictory relationship between tracking performance and lateral stability and has strong steering control robustness.
We consider the joint design of transmit beamforming and receive signal-splitting ratios in the down-link of a wireless network with simultaneous radio frequency information and energy transfer. Under constraints on t...
详细信息
We consider the joint design of transmit beamforming and receive signal-splitting ratios in the down-link of a wireless network with simultaneous radio frequency information and energy transfer. Under constraints on the signal-to-interference-plus-noise ratio at each user and the total transmit power at the base station, the design objective is to maximize either the sum harvested energy or the minimum harvested energy. We develop a computationally efficient path-following method to solve these challenging nonconvex optimization problems. We mathematically show that the proposed algorithms iteratively progress and converge to locally optimal solutions. Simulation results further show that these locally optimal solutions are the same as the globally optimal solutions for the considered practical network settings.
This paper considers signomial geometric programming (GP) dual problems, a class of nonconvex nonlinear programming problems possessing multiple locally optimal solutions, The primary purpose of this paper is to inves...
详细信息
This paper considers signomial geometric programming (GP) dual problems, a class of nonconvex nonlinear programming problems possessing multiple locally optimal solutions, The primary purpose of this paper is to investigate the quality of solutions found by use of a path-following algorithm. The path-following method may be applied to either the original nonconvex problem, or to each of a sequence of convex posynomial GP problems approximating the original problem. For each test problem, the algorithms were initiated with thousands of different starting points. It was determined that, when the stopping criterion was relaxed for early posynomial GP problems in the sequence, the ultimate solution tended to be of better quality, and more frequently globally optimal. (C) 1997 Elsevier Science B.V.
Weighted determinant maximization with linear matrix inequality constraints (maxdet-problem) is a generalization of the semidefinite programming. We give a polynomial-time complexity analysis for the path-following in...
详细信息
Weighted determinant maximization with linear matrix inequality constraints (maxdet-problem) is a generalization of the semidefinite programming. We give a polynomial-time complexity analysis for the path-following interior-point short-step and predictor-corrector methods for the maxdet-problem based on symmetric Newton equations for certain classes of scaling matrices.
Geometric programming (GP) is a class of optimization problems with a nonlinear objective function subject to a set of nonlinear constraints. The problems considered in this paper are posynomial GP problems, whose sol...
详细信息
Geometric programming (GP) is a class of optimization problems with a nonlinear objective function subject to a set of nonlinear constraints. The problems considered in this paper are posynomial GP problems, whose solutions can be obtained by solving a dual linear programing problem. The primary purpose of this paper is to investigate the quality of solutions found by, the application of a path-following algorithm, which is one of interior point methods. Two majorr approches to solve posynomial GP problems are investigated. One is dual GP as Generalized Lmear Programming (GLP) and the other is the traditional dual GP. The test results suggest that dual GP as GLP approach is advantageous over the posynomial one in the event that high a~curacy of the solution is required or critically important.
暂无评论