A new primal-dual interior-point method for semidefinite optimization based on a parameterized kernel function

In this paper, we present a class of large- and small-update primal-dual interior-point methods forsemidefinite optimization based on a parametric kernel function with a trigonometric barrier term.For both versions of the kernel-based interior-point methods, the worst case iteration bounds are established, namely,$O(n^{\frac{2}{3}}\log\frac{n}{\varepsilon})$ and $O(\sqrt{n}\log\frac{n}{\varepsilon})$, respectively.These results match the ones obtained in the linear optimization case.

Semidefinite optimization has an ever growing family of crucial applications, and large neighborhood interior point methods (IPMs) yield the method of choice to solve them. This chapter reviews the fundamental concepts and complexity results of Self-Regular (SR) IPMs for semidefinite optimizaion, that up to a log factor achieve the best polynomial complexity bound of small neighborhood IPMs. SR kernel functions are in the core of SR-IPMs. This chapter reviews several none SR kernel functions too. IPMs based on theses kernel functions enjoy similar iteration complexity bounds as SR-IPMs, though their complexity analysis requires additional tools.

In this paper, a class of large-update primal-dual interior-point methods for semidefinite optimization based on a parametric kernel function are presented. The proposed kernel function is not only used for determining the search directions but also for measuring the distance between the given iterate and the center for the algorithms. By means of the Nesterov and Todd scaling scheme, the currently best known iteration bounds for large-update methods is established.

Recently the authors introduced the notions of self-regular functions and self-regular proximity functions and used them in the design and analysis of interior-point methods (IPMs) for linear and semidefinite optimization (LO and SDO). In this paper, we consider an extension of these concepts to second-order conic optimization (SOCO). This nontrivial extension requires the development of various new tools. Versatile properties of general analytical functions associated with the second-order cone are exploited. Based on the so-called self-regular proximity functions, new primal-dual Newton methods for solving SOCO problems are proposed. It will be shown that these new large-update IPMs for SOCO enjoy polynomial ${\cal{O}}({\max\{p,q\} N^{(q+1)/{2q}}\log \frac{N}{\e}})$ iteration bounds analogous to those of their LO and SDO cousins, where N is the number of constraining cones and p,q are constants, the so-called growth degree and barrier degree of the corresponding proximity. Our analysis allows us to choose not only a constant q but also a q as large as logN. In this case, our new algorithm has the best known ${\cal{O}}({N^{1/2}\log{N}\log\frac{N}{\e}})$ iteration bound for large-update IPMs.

Exploring complexity of large update interior-point methods for [formula omitted] linear complementarity problem based on Kernel function

The purpose of this paper is to obtain new complexity results for a second-order cone optimization (SOCO) problem. We define a proximity function for the SOCO by a kernel function. Furthermore we formulate an algorithm for a large-update primal-dual interior-point method (IPM) for the SOCO by using the proximity function and give its complexity analysis, and then we show that the new worst-case iteration bound for the IPM is O(q√N(logN)q+1/q log N/e), where q?1.

The primal-dual interior point method (PDIPM) has proven to be an efficient tool for power system optimisation problems. Its computational efficiency heavily relies on sparsity techniques. Hence, when optimisation problems cannot be formulated into sparse form, PDIPM then may not be the right choice for these problems, because the computational efficiency drops significantly in factorisation of a dense matrix. A nonsparse power-system optimisation problem containing either-or constraints, pumped hydrostorage (PHS) scheduling, is presented and a two-level predictor-corrector version of PDIPM (PCPDIPM) is proposed to cope with this nonsparse and NP hard problem. To overcome the difficulty associated with the dense matrix structure, a special data transformation is proposed. By further exploiting the dense matrix structure, the performance of PCPDIPM is not deteriorated by the nonsparse structure. On the contrary, the computational efficiency is dramatically improved due to exploiting this structure. Moreover, to solve the difficulty associated with either-or constraints. An effective two-level LP procedure is proposed. To illustrate the performance of the proposed methodology, numerical results are carried out on two test cases. These results show that the presented two-level PCPDIPM solves the PHS scheduling effectively.

Based on a new parametric kernel function, this paper presents a primal-dual large-update interior-point algorithm (IPM) for semi-definite optimization (SDO) problems. The new parametric function is neither self-regular function nor the usual logarithmic barrier function. It is strongly convex and possesses some novel analytic properties. We analyse this new parametric kernel function and show that the proposed algorithm has favorable complexity bound in terms of the analytic properties of the kernel function. Moreover, the complexity bound for our large-update IPM is shown to be O(\sqrt{n}(\log n)^2 \log\frac{n}{\epsilon}). Some numerical results are reported to illustrate the feasibility of the proposed algorithm.

In this paper we present the theory and practical aspects of implementing the path following interior point methods for linear optimization, based on kernel functions. We will investigate the influence of the choice of the kernel function on the computational behavior of the generic primal-dual algorithm for Linear Optimization. We find that the finite kernel function gives the best results for more than 50 % of the tested problems compared to the standard log-barrier method.

The introduction of kernel function in primal-dual interior point methods represents not only a measure of the distance between the iterate and the central path, but also plays an important role in the amelioration of the computational complexity of an interior point algorithm. In this work, we present a primal-dual interior point method for linear optimization problems based on a new kernel function with an efficient logarithmic barrier term. We derive the complexity bounds for large and small-update methods respectively. We obtain the best known complexity bound for large-update given by Peng et al., which improves significantly the so far obtained complexity results based on a logarithmic kernel function given by El Ghami et al. The results obtained in this paper are the first with respect to the kernel function with a logarithmic barrier term.

It is known that predictor-corrector methods in a large neighborhood of the central path are among the most efficient interior-point methods (IPMs) for linear optimization (LO) problems. The best iteration bound based on the classical logarithmic barrier function is O(nlog (n/∊)). In this paper, we propose a family of self-regular proximity-based predictor-corrector (SRPC) IPMs for LO in a large neighborhood of the central path. In the predictor step, we use either an affine scaling or a self-regular direction; in the corrector step, we use always a self-regular direction. Our new algorithms use a special proximity function with different search directions and thus allows us to improve the so far best theoretical iteration complexity for a family of SRPC IPMs. An O\((\sqrt{n}{\exp} ((1 - q + {\log} n)/2) {\log} n {\log} (n/\epsilon))\) worst-case iteration bound with quadratic convergence is established, where q is the barrier degree of the SR proximity function.

An interior-point algorithm for linear optimization based on a new barrier function

We are concerned with structural optimization problems in CFD where the state variables are supposed to satisfy a linear or nonlinear Stokes system and the design variables are subject to bilateral pointwise constraints. Within a primal-dual setting, we suggest an all-at-once approach based on interior-point methods. The discretization is taken care of by Taylor-Hood elements with respect to a simplicial triangulation of the computational domain. The efficient numerical solution of the discretized problem relies on adaptive path-following techniques featuring a predictor-corrector scheme with inexact Newton solves of the KKT system by means of an iterative null-space approach. The performance of the suggested method is documented by several illustrative numerical examples.

This study aims to improve the computation of the search direction in the primal-dual Interior Point Method through preconditioned iterative methods. It is about a hybrid approach that combines the Controlled Cholesky Factorization preconditioner and the Splitting preconditioner. This approach has shown good results, however, in these preconditioners there are factors that reduce their efficiency, such as faults on the diagonal when performing the Cholesky factorization, as well as a demand for excessive memory, among others. Thus, some modifications are proposed in these preconditioners, as well as a new phase change, in order toimprove the performance of the hybrid preconditioner. In the Controlled Cholesky Factorization, the parameters that control the filling and the correction of the faults which occur on the diagonal are modified. It considers the relationship between the components from Controlled Cholesky Factorization obtained before and after the fault on the diagonal. In the Splitting preconditioner, in turn, a sparse base is constructed through an appropriate ordering of the columns from constrained matrix optimization problem. In addition, a theoretical result is presented, which shows that, with the proposed ordering, the condition number of the preconditioned Normal Equation matrix with the Splitting preconditioner is uniformly limited by an amount that depends only on the original data of the problem and not on the iteration of the Interior Point Method. Numerical experiments with large scale problems, corroborate the robustness and computational efficiency from this approach.

As recently demonstrated by the study of the primal-dual interior-point methods based on kernel functions, a kernel function not only serves to determine the search direction and measure the distance of the current iteration point to the µ-center, but also affects the iteration complexity and the practical computational efficiency of the algorithm.In this paper, we propose a primal-dual interior-point method for a linear optimization based on a new parameterized kernel function.The construction of the new parameterized kernel function is motivated by the parameterized ways of existing kernel functions.By using properties of the new parameterized kernel function, we improve the iteration bound of the large-update method from O(n, which is the best theoretical iteration result currently known.Finally, some numerical results are given to present the efficiency and potential of our kernel function.