A Combined Homotopy Infeasible Interior-Point Method for Convex Nonlinear Programming

In this paper, on the basis of the logarithmic barrier function and KKT conditions, we propose a combined homotopy infeasible interior-point method （CHIIP） for convex nonlinear programming problems. For any convex nonlinear programming, without strict convexity for the logarithmic barrier function, we get different solutions of the convex programming in different cases by CHIIP method.

A New Infeasible Interior-point Method for Linear Complementarity Problem Based on Full Newton Step

This paper proposes an infeasible interior-point algorithm with full-Newton step for linear complementarity problem,which is an extension of Roos about linear optimization. The main iteration of the algorithm consists of a feasibility step and several centrality steps. At last,we prove that the algorithm has O(nlog n/ε) polynomial complexity,which coincides with the best known one for the infeasible interior-point algorithm at present.

A novel PID controller tuning method based on optimization technique

An approach for parameter estimation of proportional-integral-derivative(PID) control system using a new nonlinear programming(NLP) algorithm was proposed.SQP/IIPM algorithm is a sequential quadratic programming(SQP) based algorithm that derives its search directions by solving quadratic programming(QP) subproblems via an infeasible interior point method(IIPM) and evaluates step length adaptively via a simple line search and/or a quadratic search algorithm depending on the termination of the IIPM solver.The task of tuning PI/PID parameters for the first-and second-order systems was modeled as constrained NLP problem. SQP/IIPM algorithm was applied to determining the optimum parameters for the PI/PID control systems.To assess the performance of the proposed method,a Matlab simulation of PID controller tuning was conducted to compare the proposed SQP/IIPM algorithm with the gain and phase margin(GPM) method and Ziegler-Nichols(ZN) method.The results reveal that,for both step and impulse response tests,the PI/PID controller using SQP/IIPM optimization algorithm consistently reduce rise time,settling-time and remarkably lower overshoot compared to GPM and ZN methods,and the proposed method improves the robustness and effectiveness of numerical optimization of PID control systems.

A Modified and Simplified Full Nesterov–Todd Step O(N)Infeasible Interior-Point Method for Second-Order Cone Optimization

We present a modified and simplified version of an infeasible interior-point method for second-order cone optimization published in 2013(Zangiabadi et al.in J Optim Theory Appl,2013).In the earlier version,each iteration consisted of one socalled feasibility step and a few centering steps.Here,each iteration consists of only a feasibility step.Thus,the new algorithm improves the number of iterations and the improvement is due to a lemma which gives an upper bound for the proximity after the feasibility step.The complexity result coincides with the best-known iteration bound for infeasible interior-point methods.

A FULL-NEWTON STEP INFEASIBLE INTERIOR-POINT ALGORITHM FOR P_*(κ) LINEAR COMPLEMENTARITY PROBLEM

This paper proposes a new infeasible interior-point algorithm with full-Newton steps for P_*(κ) linear complementarity problem(LCP),which is an extension of the work by Roos(SIAM J.Optim.,2006,16(4):1110-1136).The main iteration consists of a feasibility step and several centrality steps.The authors introduce a specific kernel function instead of the classic logarithmical barrier function to induce the feasibility step,so the analysis of the feasibility step is different from that of Roos' s.This kernel function has a finite value on the boundary.The result of iteration complexity coincides with the currently known best one for infeasible interior-point methods for P_*(κ) LCP.Some numerical results are reported as well.

A New Class of Infeasible InteriorPoint Algorithm for Linear Complementarity Problem

This paper proposes an infeasible interior-point algorithm for linear complementarity problem with full-Newton steps.The main iteration consists of a feasibility step and several centrality steps.No more than O（n log（n /ε））iterations are required for getting ε-solution of the problem at hand,which coincides with the best-known bound for infeasible interior-point algorithms.

A Path-Following Full Newton-Step Infeasible Interior-Point Algorithm for P∗(κ)-HLCPs Based on a Kernel Function

In this paper,we present a path-following infeasible interior-point method for P∗(κ)horizontal linear complementarity problems(P∗(κ)-HLCPs).The algorithm is based on a simple kernel function for finding the search directions and defining the neighborhood of the central path.The algorithm follows the central path related to some perturbations of the original problem,using the so-called feasibility and centering steps,along with only full such steps.Therefore,it has the advantage that the calculation of the step sizes at each iteration is avoided.The complexity result shows that the full-Newton step infeasible interior-point algorithm based on the simple kernel function enjoys the best-known iteration complexity for P∗(κ)-HLCPs.

Primal Infeasible-interior-point Algorithm for Locating Weighted Analytic Center

The quadratic penalty function is considered for finding the weighted analytic center ofa polytope.By an-alyzing the properties of the penalty function,an exterior central path is introduced.It is shown that the exterior cen-tral path has some similar properties as the interior one in a line ar programming case,If the starting point is close to the path,by following the path with an appropriate step,a polynomia1 algorithm is derived.Usually,the starting point is not in the polytope,the method can be seen as an infeasible interior point one.It is also discussed how to get an appropriate starting point.