A Mizuno-Todd-Ye predictor-corrector infeasible-interior-point method for linear programming over symmetric cones

We propose a new general type of splitting methods for accretive operators in Banach spaces. We then give the sufficient conditions to guarantee the strong convergence. In the last section, we apply our results to the minimization optimization problem and the linear inverse problem including the numerical examples.     

An infeasible-interior-point potential-reduction algorithm for linear programming

n . The method is based on Rockafellar’s proximal point algorithm and a cutting-plane technique. At each step, we use an approximate proximal point p^a(x_k) of x_k to define a v_k∈∂_εkf(p^a(x_k)) with ε_k≤α∥v_k∥, where α is a constant. The method monitors the reduction in the value of ∥v_k∥ to identify when a line search on f should be used. The quasi-Newton step is used to reduce the value of ∥v_k∥. Without the differentiability of f, the method converges globally and the rate of convergence is Q-linear. Superlinear convergence is also discussed to extend the characterization result of Dennis and Moré. Numerical results show the good performance of the method. Received October 3, 1995 / Revised version received August 20, 1998 Published online January 20, 1999     

A primal—dual infeasible-interior-point algorithm for linear programming

As in many primal—dual interior-point algorithms, a primal—dual infeasible-interior-point algorithm chooses a new point along the Newton direction towards a point on the central trajectory, but it does not confine the iterates within the feasible region. This paper proposes a step length rule with which the algorithm takes large distinct step lengths in the primal and dual spaces and enjoys the global convergence.Part of this research was done when M. Kojima and S. Mizuno visited at the IBM Almaden Research Center. Partial support from the Office of Naval Research under Contract N00014-91-C-0026 is acknowledged.Supported by Grant-in-Aids for Co-operative Research (03832017) of The Japan Ministry of Education, Science and Culture.Supported by Grant-in-Aids for Encouragement of Young Scientist (03740125) and Co-operative Research (03832017) of The Japan Ministry of Education, Science and Culture.     

Feasibility and solvability of Lyapunov-type linear programming over symmetric cones

In this paper, we consider the Lyapunov-type linear programming and its dual over symmetric cones. By introducing and characterizing the generalized inverse of Lyapunov operator in Euclidean Jordan algebras, we establish two kinds of Lyapunov-type Farkas’ lemmas to exhibit feasibilities of the corresponding primal and dual programming problems, respectively. As one of the main results, we show that the feasibilities of the primal and dual problems lead to the solvability of the primal problem and zero duality gap under some mild condition. In this case, we obtain that any solution to the pair of primal and dual problems is equivalent to the solution of the corresponding KKT system.     

A smoothing Newton algorithm based on a one-parametric class of smoothing functions for linear programming over symmetric cones

In this paper, we introduce a one-parametric class of smoothing functions which contains the Fischer–Burmeister smoothing function and the CHKS smoothing function as special cases. Based on this class of smoothing functions, a smoothing Newton algorithm is extended to solve linear programming over symmetric cones. The global and local quadratic convergence results of the algorithm are established under suitable assumptions. The theory of Euclidean Jordan algebras is a basic tool in our analysis.     

Understanding linear semi-infinite programming via linear programming over cones

In this article, the standard primal and dual linear semi-infinite programming (DLSIP) problems are reformulated as linear programming (LP) problems over cones. Therefore, the dual formulation via the minimal cone approach, which results in zero duality gap for the primal–dual pair for LP problems over cones, can be applied to linear semi-infinite programming (LSIP) problems. Results on the geometry of the set of the feasible solutions for the primal LSIP problem and the optimality criteria for the DLSIP problem are also discussed.     

Polynomiality of infeasible-interior-point algorithms for linear programming

Kojima, Megiddo, and Mizuno investigate an infeasible-interior-point algorithm for solving a primal—dual pair of linear programming problems and they demonstrate its global convergence. Their algorithm finds approximate optimal solutions of the pair if both problems have interior points, and they detect infeasibility when the sequence of iterates diverges. Zhang proves polynomial-time convergence of an infeasible-interior-point algorithm under the assumption that both primal and dual problems have feasible points. In this paper, we show that a modification of the Kojima—Megiddo—Mizuno algorithm solves the pair of problems in polynomial time without assuming the existence of the LP solution. Furthermore, we develop anO(nL)-iteration complexity result for a variant of the algorithm.The original title was Polynomiality of the Kojima—Megiddo—Mizuno infeasible-interior-point algorithm for linear programming.Research performed while visiting Cornell University (April 1992 – January 1993) as an Overseas Research Scholar of the Ministry of Science, Education and Culture of Japan.     

A new polynomial-time algorithm for linear programming

We present a new polynomial-time algorithm for linear programming. In the worst case, the algorithm requiresO(n ^3.5 L) arithmetic operations onO(L) bit numbers, wheren is the number of variables andL is the number of bits in the input. The running-time of this algorithm is better than the ellipsoid algorithm by a factor ofO(n ^2.5). We prove that given a polytopeP and a strictly interior point a εP, there is a projective transformation of the space that mapsP, a toP′, a′ having the following property. The ratio of the radius of the smallest sphere with center a′, containingP′ to the radius of the largest sphere with center a′ contained inP′ isO(n). The algorithm consists of repeated application of such projective transformations each followed by optimization over an inscribed sphere to create a sequence of points which converges to the optimal solution in polynomial time. This is a substantially revised version of the paper presented at the Symposium on Theory of Computing, Washington D. C., April 1984.     

Superlinear convergence of infeasible-interior-point methods for linear programming

At present the interior-point methods of choice are primal—dual infeasible-interior-point methods, where the iterates are kept positive, but allowed to be infeasible. In practice, these methods have demonstrated superior computational performance. From a theoretical point of view, however, they have not been as thoroughly studied as their counterparts — feasible-interior-point methods, where the iterates are required to be strictly feasible. Recently, Kojima et al., Zhang, Mizuno and Potra studied the global convergence of algorithms in the primal—dual infeasible-interior-point framework. In this paper, we continue to study this framework, and in particular we study the local convergence properties of algorithms in this framework. We construct parameter selections that lead toQ-superlinear convergence for a merit function andR-superlinear convergence for the iteration sequence, both at rate 1 + where can be arbitrarily close to one.Research supported in part by NSF DMS-9102761 and DOE DE-FG05-91ER25100.Corresponding author.     

An infeasible-interior-point algorithm for linear complementarity problems

We modify the algorithm of Zhang to obtain anO(n²L) infeasible-interior-point algorithm for monotone linear complementarity problems that has an asymptoticQ-subquadratic convergence rate. The algorithm requires the solution of at most two linear systems with the same coefficient matrix at each iteration.This research was supported by the Office of Scientific Computing, U.S. Department of Energy, under Contract W-31-109-Eng-38.     

一个新的求解线性规划最小二乘算法

§1Introduction Currently,therearetwopopularapproachesinlinearprogramming:pivotalgorithm andinterior-pointalgorithm.Manyoftheirvariantsdevelopedbothintheoryand applicationsarestillinprogress.Thepivotmethodobtainstheoptimalsolutionviamoving consecutivelytoabettercorner-pointinthefeasibleregion,anditsmodificationstryto improvethespeedofattainingtheoptimality.Incontrast,theinterior-pointalgorithmis claimedasaninterior-pointapproach,whichgoesfromafeasiblepointtoafeasiblepoint throughtheinterioroft…     

A new class of smoothing complementarity functions over symmetric cones

A popular approach to solving the complementarity problem is to reformulate it as an equivalent system of smooth equations via a smoothing complementarity function. In this paper, first we propose a new class of smoothing complementarity functions, which contains the natural residual smoothing function and the Fischer–Burmeister smoothing function for symmetric cone complementarity problems. Then we give some unified formulae of the Fréchet derivatives associated with Jordan product. Finally, the derivative of the new proposed class of smoothing complementarity functions is deduced over symmetric cones.     

An arc-search $${\mathcal {O}}(nL)$$ infeasible-interior-point algorithm for linear programming

Arc-search interior-point methods have been proposed to capture the curvature of the central path using an approximation based on ellipse. Yang et al. (J Appl Math Comput 51(1–2):209–225, 2016) proved that an arc-search algorithm has the computational order of \({\mathcal {O}}(n^{5/4}L)\). In this paper, we propose an arc-search infeasible-interior-point algorithms and discuss its convergence analysis. We improve the polynomial bound from \({\mathcal {O}}(n^{5/4}L)\) to \({\mathcal {O}}(nL)\), which is at least as good as the best existing bound for infeasible-interior-point algorithms for linear programming. Numerical results indicate that the proposed method solved LP instances faster than the existing \({\mathcal {O}}(n^{5/4}L)\) method.     

On the Lipschitzian property in linear complementarity problems over symmetric cones

Let V be a Euclidean Jordan algebra with symmetric cone K. We show that if a linear transformation L on V has the Lipschitzian property and the linear complementarity problem LCP(L,q) over K has a solution for every invertible q∈V, then 〈L(c),c〉>0 for all primitive idempotents c in V. We show that the converse holds for Lyapunov-like transformations, Stein transformations and quadratic representations. We also show that the Lipschitzian Q-property of the relaxation transformation R_A on V implies that A is a P-matrix.     

Lyapunov-type least-squares problems over symmetric cones

The Lyapunov-type least-squares problem over symmetric cone is to find the least-squares solution of the Lyapunov equation with a constraint of symmetric cone in the Euclidean Jordan algebra, and it contains the Lyapunov-type least-squares problem over cone of semidefinite matrices as a special case. In this paper, we first give a detailed analysis for the image of Lyapunov operator in the Euclidean Jordan algebra. Relying on these properties together with some characterizations of symmetric cone, we then establish some necessary and?or sufficient conditions for solution existence of the Lyapunov-type least-squares problem. Finally, we study uniqueness of the least-squares solution.     

Smoothing algorithms for complementarity problems over symmetric cones

There recently has been much interest in studying optimization problems over symmetric cones. In this paper, we first investigate a smoothing function in the context of symmetric cones and show that it is coercive under suitable assumptions. We then extend two generic frameworks of smoothing algorithms to solve the complementarity problems over symmetric cones, and prove the proposed algorithms are globally convergent under suitable assumptions. We also give a specific smoothing Newton algorithm which is globally and locally quadratically convergent under suitable assumptions. The theory of Euclidean Jordan algebras is a basic tool which is extensively used in our analysis. Preliminary numerical results for second-order cone complementarity problems are reported.