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...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.展开更多
In this paper, we design a primal-dual interior-point algorithm for linear optimization. Search directions and proximity function are proposed based on a new kernel function which includes neither growth term nor barr...In this paper, we design a primal-dual interior-point algorithm for linear optimization. Search directions and proximity function are proposed based on a new kernel function which includes neither growth term nor barrier term. Iteration bounds both for large-and small-update methods are derived, namely, O(nlog(n/c)) and O(√nlog(n/ε)). This new kernel function has simple algebraic expression and the proximity function has not been used before. Analogous to the classical logarithmic kernel function, our complexity analysis is easier than the other pri- mal-dual interior-point methods based on logarithmic barrier functions and recent kernel functions.展开更多
The paper presents a technique for solving the binary linear programming model in polynomial time. The general binary linear programming problem is transformed into a convex quadratic programming problem. The convex q...The paper presents a technique for solving the binary linear programming model in polynomial time. The general binary linear programming problem is transformed into a convex quadratic programming problem. The convex quadratic programming problem is then solved by interior point algorithms. This settles one of the open problems of whether P = NP or not. The worst case complexity of interior point algorithms for the convex quadratic problem is polynomial. It can also be shown that every liner integer problem can be converted into binary linear problem.展开更多
Airplane refueling problem is a nonlinear unconstrained optimization problem.Given a fleet of n airplanes with mid-air refueling technique,the question is to find the best refueling policy to make the last remaining a...Airplane refueling problem is a nonlinear unconstrained optimization problem.Given a fleet of n airplanes with mid-air refueling technique,the question is to find the best refueling policy to make the last remaining airplane travel the farthest.At first,we proposed the definition of sequential feasible solution.We proved that if an airplane refueling instance has feasible solutions,it must have sequential feasible solutions;and the optimal feasible solution must be the optimal sequential feasible solution.Then we proved that any airplane refueling instance has n!feasible solutions,and has at most 2n-2sequential feasible solutions.So we proposed the sequential search algorithm which aims to seek out all of the sequential feasible solutions and to search for the maximal sequential feasible solution by bubble sorting all of the sequential feasible solutions.We observed that the number of the sequential feasible solutions will change to grow at a polynomial rate when n is greater than an inflection point N.Moreover,we built an efficient computability scheme,according to which we could forecast within a polynomial time the computational complexity of the sequential search algorithm that runs on any given airplane refueling instance.展开更多
In this paper, we propose a theoretical framework of an infeasible interior-point algorithm for solving monotone linear cornplementarity problems over symmetric cones (SCLCP). The new algorithm gets Newton-like dire...In this paper, we propose a theoretical framework of an infeasible interior-point algorithm for solving monotone linear cornplementarity problems over symmetric cones (SCLCP). The new algorithm gets Newton-like directions from the Chen-Harker-Kanzow-Smale (CHKS) smoothing equation of the SCLCP. It possesses the following features: The starting point is easily chosen; one approximate Newton step is computed and accepted at each iteration; the iterative point with unit stepsize automatically remains in the neighborhood of central path; the iterative sequence is bounded and possesses (9(rL) polynomial-time complexity under the monotonicity and solvability of the SCLCP.展开更多
In this paper we propose an efficient and robust method for computing the analytic center of the polyhedral set P={x€R^n|Ax=b,x>0},where the matrix A€ Rm×n is ill-conditioned,and there are errors in A and b.Be...In this paper we propose an efficient and robust method for computing the analytic center of the polyhedral set P={x€R^n|Ax=b,x>0},where the matrix A€ Rm×n is ill-conditioned,and there are errors in A and b.Besides overcoming the difficulties caused by ill-cond计ioning of the matrix A and errors in A and b,our method can also detect the infeasibility and the unboundedness of the polyhedral set P automatically during the compu tation.Det ailed mat hematical analyses for our method are presen ted and the worst case complexity of the algorithm is also given.Finally some numerical results are presented to show the robustness and effectiveness of the new method.展开更多
基金Supported by the National Natural Science Foundation of China(71471102)
摘要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.
基金Supported by the Natural Science Foundation of Hubei Province (2008CDZD47)
摘要In this paper, we design a primal-dual interior-point algorithm for linear optimization. Search directions and proximity function are proposed based on a new kernel function which includes neither growth term nor barrier term. Iteration bounds both for large-and small-update methods are derived, namely, O(nlog(n/c)) and O(√nlog(n/ε)). This new kernel function has simple algebraic expression and the proximity function has not been used before. Analogous to the classical logarithmic kernel function, our complexity analysis is easier than the other pri- mal-dual interior-point methods based on logarithmic barrier functions and recent kernel functions.
摘要The paper presents a technique for solving the binary linear programming model in polynomial time. The general binary linear programming problem is transformed into a convex quadratic programming problem. The convex quadratic programming problem is then solved by interior point algorithms. This settles one of the open problems of whether P = NP or not. The worst case complexity of interior point algorithms for the convex quadratic problem is polynomial. It can also be shown that every liner integer problem can be converted into binary linear problem.
摘要Airplane refueling problem is a nonlinear unconstrained optimization problem.Given a fleet of n airplanes with mid-air refueling technique,the question is to find the best refueling policy to make the last remaining airplane travel the farthest.At first,we proposed the definition of sequential feasible solution.We proved that if an airplane refueling instance has feasible solutions,it must have sequential feasible solutions;and the optimal feasible solution must be the optimal sequential feasible solution.Then we proved that any airplane refueling instance has n!feasible solutions,and has at most 2n-2sequential feasible solutions.So we proposed the sequential search algorithm which aims to seek out all of the sequential feasible solutions and to search for the maximal sequential feasible solution by bubble sorting all of the sequential feasible solutions.We observed that the number of the sequential feasible solutions will change to grow at a polynomial rate when n is greater than an inflection point N.Moreover,we built an efficient computability scheme,according to which we could forecast within a polynomial time the computational complexity of the sequential search algorithm that runs on any given airplane refueling instance.
基金Supported by the National Natural Science Foundation of China(No.10671010)Specialized Research Fund for the Doctoral Program of Higher Education(200800040024)
摘要In this paper, we propose a theoretical framework of an infeasible interior-point algorithm for solving monotone linear cornplementarity problems over symmetric cones (SCLCP). The new algorithm gets Newton-like directions from the Chen-Harker-Kanzow-Smale (CHKS) smoothing equation of the SCLCP. It possesses the following features: The starting point is easily chosen; one approximate Newton step is computed and accepted at each iteration; the iterative point with unit stepsize automatically remains in the neighborhood of central path; the iterative sequence is bounded and possesses (9(rL) polynomial-time complexity under the monotonicity and solvability of the SCLCP.
基金The authors would like to thank two anonymous referees for their valuable comments and suggestions.The author Yu-hong Dai is supported by the Chinese Natural Science Foundation(Nos.11631013,71331001 and 11331012)the National 973 Program of China(No.2015CB856002)The author Fengmin Xu is supported by the Chinese NSF grants(Nos.11571271,11631013 and 11605139).
摘要In this paper we propose an efficient and robust method for computing the analytic center of the polyhedral set P={x€R^n|Ax=b,x>0},where the matrix A€ Rm×n is ill-conditioned,and there are errors in A and b.Besides overcoming the difficulties caused by ill-cond计ioning of the matrix A and errors in A and b,our method can also detect the infeasibility and the unboundedness of the polyhedral set P automatically during the compu tation.Det ailed mat hematical analyses for our method are presen ted and the worst case complexity of the algorithm is also given.Finally some numerical results are presented to show the robustness and effectiveness of the new method.