Primal-dual algorithms for linear programming based on the logarithmic barrier method

In this paper, we deal with primal-dual interior point methods for solving the linear programming problem. We present a short-step and a long-step path-following primal-dual method and derive polynomial-time bounds for both methods. The iteration bounds are as usual in the existing literature, namely iterations for the short-step variant andO(nL) for the long-step variant. In the analysis of both variants, we use a new proximity measure, which is closely related to the Euclidean norm of the scaled search direction vectors. The analysis of the long-step method depends strongly on the fact that the usual search directions form a descent direction for the so-called primal-dual logarithmic barrier function.This work was supported by a research grant from Shell, by the Dutch Organization for Scientific Research (NWO) Grant 611-304-028, by the Hungarian National Research Foundation Grant OTKA-2116, and by the Swiss National Foundation for Scientific Research Grant 12-26434.89.     

基于中心路径大邻域上的一类非单调线性互补问题的高阶可行内点算法

In this paper a high-order feasible interior point algorithm for a class of nonmonotonic (P-matrix) linear complementary problem based on large neighborhoods of central path is presented and its iteration complexity is discussed.These algorithms are implicitly associated with a large neighborhood whose size may depend on the dimension of the problems. The complexity of these algorithms bound depends on the size of the neighborhood. It is well known that the complexity of large-step algorithms is greater than that of short- step ones. By using high-order power series (hence the name high-order algorithms), the iteration complexity can be reduced. We show that the upper bound of complexity for our high-order algorithms is equal to that for short-step algorithms.     

Long-Step Primal Path-Following Algorithm for Monotone Variational Inequality Problems

In this paper, we present a long-step primal path-following algorithm and prove its global convergence under usual assumptions. It is seen that the short-step algorithm is a special case of the long-step algorithm for a specific selection of the parameters and the initial solution. Our theoretical result indicates that the long-step algorithm is more flexible. Numerical results indicate that the long-step algorithm converges faster than the short-step algorithm.     

A wide neighborhood interior-point algorithm for linear optimization based on a specific kernel function

This paper presents an interior point algorithm for solving linear optimization problems in a wide neighborhood of the central path introduced by Ai and Zhang (SIAM J Optim 16:400–417, 2005). In each iteration, the algorithm computes the new search directions by using a specific kernel function. The convergence of the algorithm is shown and it is proved that the algorithm has the same iteration bound as the best short-step algorithms. We demonstrate the computational efficiency of the proposed algorithm by testing some Netlib problems in standard form. To best our knowledge, this is the first wide neighborhood path-following interior-point method with the same complexity as the best small neighborhood path-following interior-point methods that uses the kernel function.

     

Primal interior-point decomposition algorithms for two-stage stochastic extended second-order cone programming

ABSTRACT

We study and solve the two-stage stochastic extended second-order cone programming problem. We show that the barrier recourse functions and the composite barrier functions for this optimization problem are self-concordant families with respect to barrier parameters. These results are used to develop primal decomposition-based interior-point algorithms. The worst case iteration complexity of the developed algorithms is shown to be the same as that for the short- and long-step primal interior algorithms applied to the extensive formulation of our problem.     

Complexity analysis and numerical implementation of a short-step primal-dual algorithm for linear complementarity problems

In this paper we deal with the study of the polynomial complexity and numerical implementation for a short-step primal-dual interior point algorithm for monotone linear complementarity problems LCP. The analysis is based on a new class of search directions used by the author for convex quadratic programming (CQP) [M. Achache, A new primal-dual path-following method for convex quadratic programming, Computational and Applied Mathematics 25 (1) (2006) 97-110]. Here, we show that this algorithm enjoys the best theoretical polynomial complexity namely , iteration bound. For its numerical performances some strategies are used. Finally, we have tested this algorithm on some monotone linear complementarity problems.     

基于一类新方向的宽邻域路径跟踪内点算法

基于一类带有参数theta的新方向, 提出了求解单调线性互补问题的宽邻 域路径跟踪内点算法, 且当theta=1时即为经典牛顿方向. 当取theta为与问题规模 n无关的常数时, 算法具有O(nL)迭代复杂性, 其中L是输入数据的长度, 这与经典宽邻 域算法的复杂性相同; 当取theta=\sqrt{n/\beta\tau}时, 算法具有O(\sqrt{n}L)迭代复杂性, 这里的\beta, \tau是邻域参数, 这与窄邻域算法的复杂性相同. 这是首次研究包括经典宽邻域路径跟踪算法的一类内点算法, 给出了统一的算法框架和收敛性分析方法.     

Path-following interior point algorithms for the Cartesian <Emphasis Type="Italic">P</Emphasis><Subscript>*</Subscript>(κ)-LCP over symmetric cones

In this paper, we establish a theoretical framework of path-following interior point algorithms for the linear complementarity problems over symmetric cones (SCLCP) with the Cartesian P _*(κ)-property, a weaker condition than the monotonicity. Based on the Nesterov-Todd, xy and yx directions employed as commutative search directions for semidefinite programming, we extend the variants of the short-, semilong-, and long-step path-following algorithms for symmetric conic linear programming proposed by Schmieta and Alizadeh to the Cartesian P _*(κ)-SCLCP, and particularly show the global convergence and the iteration complexities of the proposed algorithms. This work was supported by National Natural Science Foundation of China (Grant Nos. 10671010, 70841008)     

New Self-Concordant Barrier for the Hypercube

We introduce a new barrier function to build new interior-point algorithms to solve optimization problems with bounded variables. First, we show that this function is a (3/2)n-self-concordant barrier for the unitary hypercube [0,1] ⁿ , assuring thus the polynomial property of related algorithms. Second, using the Hessian metric of that barrier, we present new explicit algorithms from the point of view of Riemannian geometry applications. Third, we prove that the central path defined by the new barrier to solve a certain class of linearly constrained convex problems maintains most of the properties of the central path defined by the usual logarithmic barrier. We present also a primal long-step path-following algorithm with similar complexity to the classical barrier. Finally, we introduce a new proximal-point Bregman type algorithm to solve linear problems in [0,1] ⁿ and prove its convergence. P.R. Oliveira was partially supported by CNPq/Brazil.     

Neighborhood-following algorithms for linear programming

In this paper, we present neighborhood-following algorithms for linear programming. When the neighborhood is a wide neighborhood, our algorithms are wide neighborhood primal-dual interior point algorithms. If the neighborhood degenerates into the central path, our algorithms also degenerate into path-following algorithms. We prove that our algorithms maintain the O(n~(1/2)L)-iteration complexity still, while the classical wide neighborhood primal-dual interior point algorithms have only the O(nL)-iteration complexity. We also proved that the algorithms are quadratic convergence if the optimal vertex is nondegenerate. Finally, we show some computational results of our algorithms.     

Minibatch stochastic subgradient-based projection algorithms for feasibility problems with convex inequalities

In this paper we consider convex feasibility problems where the feasible set is given as the intersection of a collection of closed convex sets. We assume that each set is specified algebraically as a convex inequality, where the associated convex function is general (possibly non-differentiable). For finding a point satisfying all the convex inequalities we design and analyze random projection algorithms using special subgradient iterations and extrapolated stepsizes. Moreover, the iterate updates are performed based on parallel random observations of several constraint components. For these minibatch stochastic subgradient-based projection methods we prove sublinear convergence results and, under some linear regularity condition for the functional constraints, we prove linear convergence rates. We also derive sufficient conditions under which these rates depend explicitly on the minibatch size. To the best of our knowledge, this work is the first deriving conditions that show theoretically when minibatch stochastic subgradient-based projection updates have a better complexity than their single-sample variants when parallel computing is used to implement the minibatch. Numerical results also show a better performance of our minibatch scheme over its non-minibatch counterpart.

     

Complexity of Interior Point Methods for a Class of Linear Complementarity Problems Using a Kernel Function with Trigonometric Growth Term

In this paper, we propose a large-update primal-dual interior point method for solving a class of linear complementarity problems based on a new kernel function. The main aspects distinguishing our proposed kernel function from the others are as follows: Firstly, it incorporates a specific trigonometric function in its growth term, and secondly, the corresponding barrier term takes finite values at the boundary of the feasible region. We show that, by resorting to relatively simple techniques, the primal-dual interior point methods designed for a specific class of linear complementarity problems enjoy the so-called best-known iteration complexity for the large-update methods.     

A quadratically convergent polynomial long-step algorithm for A class of nonlinear monotone complementarity problems *

Several interior point algorithms have been proposed for solving nonlinear monotone complementarity problems. Some of them have polynomial worst-case complexity but have to confine to short steps, whereas some of the others can take long steps but no polynomial complexity is proven. This paper presents an algorithm which is both long-step and polynomial. In addition, the sequence generated by the algorithm, as well as the corresponding complementarity gap, converges quadratically. The proof of the polynomial complexity requires that the monotone mapping satisfies a scaled Lipschitz condition, while the quadratic rate of convergence is derived under the assumptions that the problem has a strictly complementary solution and that the Jacobian of the mapping satisfies certain regularity conditions     

单调线性互补问题的Mehrotra型预估-校正算法的迭代复杂性

Mehrotra型预估-校正算法是很多内点算法软件包的算法基础,但它的多项式迭代复杂性直到2007年才被Salahi等人证明.通过选择一个固定的预估步长及与Salahi文中不同的校正方向,本文把Salahi等人的算法拓展到单调线性互补问题,使得新算法的迭代复杂性为O(n log((x0)T s0/ε)),同时,初步的数值实验证明了新算法是有效的.     

On a Commutative Class of Search Directions for Linear Programming over Symmetric Cones

The commutative class of search directions for semidefinite programming was first proposed by Monteiro and Zhang (Ref. 1). In this paper, we investigate the corresponding class of search directions for linear programming over symmetric cones, which is a class of convex optimization problems including linear programming, second-order cone programming, and semidefinite programming as special cases. Complexity results are established for short-step, semilong-step, and long-step algorithms. Then, we propose a subclass of the commutative class for which we can prove polynomial complexities of the interior-point method using semilong steps and long steps. This subclass still contains the Nesterov–Todd direction and the Helmberg–Rendl–Vanderbei–Wolkowicz/Kojima–Shindoh–Hara/Monteiro direction. An explicit formula to calculate any member of the class is also given.     

Projected orthogonal vectors in two-dimensional search interior point algorithms for linear programming

The vast majority of linear programming interior point algorithms successively move from an interior solution to an improved interior solution by following a single search direction, which corresponds to solving a one-dimensional subspace linear program at each iteration. On the other hand, two-dimensional search interior point algorithms select two search directions, and determine a new and improved interior solution by solving a two-dimensional subspace linear program at each step. This paper presents primal and dual two-dimensional search interior point algorithms derived from affine and logarithmic barrier search directions. Both search directions are determined by randomly partitioning the objective function into two orthogonal vectors. Computational experiments performed on benchmark instances demonstrate that these new methods improve the average CPU time by approximately 12% and the average number of iterations by 14%.

     

An {O(\sqrt{n}L)} iteration primal-dual second-order corrector algorithm for linear programming

In this paper, we propose a primal-dual second-order corrector interior point algorithm for linear programming problems. At each iteration, the method computes a corrector direction in addition to the Ai–Zhang direction [Ai and Zhang in SIAM J Optim 16:400–417 (2005)], in an attempt to improve performance. The corrector is multiplied by the square of the step-size in the expression of the new iterate. We prove that the use of the corrector step does not cause any loss in the worst-case complexity of the algorithm. To our best knowledge, this is the first wide neighborhood second-order corrector algorithm enjoyed the low iteration bound of O(?nL){O(\sqrt{n}L)}, the same as the best known complexity results for interior point methods.     

Interior Point Methods for Second-Order Cone Programming and OR Applications

Interior point methods (IPM) have been developed for all types of constrained optimization problems. In this work the extension of IPM to second order cone programming (SOCP) is studied based on the work of Andersen, Roos, and Terlaky. SOCP minimizes a linear objective function over the direct product of quadratic cones, rotated quadratic cones, and an affine set. It is described in detail how to convert several application problems to SOCP. Moreover, a proof is given of the existence of the step for the infeasible long-step path-following method. Furthermore, variants are developed of both long-step path-following and of predictor-corrector algorithms. Numerical results are presented and analyzed for those variants using test cases obtained from a number of application problems.     

竞争市场均衡问题的内点算法

本文应用最优化方法求解经济学中的经典问题-竞争市场均衡问题.本文对Ye的算法(Ye首先提出了解Fisher问题的原始-对偶路径跟踪算法)做了改进,分别给出了步长调整和迭代方向分解后的原始-对偶路径跟踪算法,并对算法做了理论证明和复杂性分析.最后分析了初始点的求法,做了初步的数值计算.计算结果表明算法能在有效时间内求得问题的解.     

An infeasible primal-dual interior point algorithm for linear programs based on logarithmic equivalent transformation

In this paper, we analyze the effect of making algebraically equivalent transformations for the standard centering equation Xs=μe, and specifically consider two cases: power transformation and logarithmic transformation. Especially, for the last case, an infeasible long-step primal-dual path following interior point algorithm is developed, and its global convergence analysis and polynomial-time complexity bound are also given.