Interior-point Implementation Research Articles

A structured modified Newton approach for solving systems of nonlinear equations arising in interior-point methods for quadratic programming

AbstractThe focus in this work is on interior-point methods for inequality-constrained quadratic programs, and particularly on the system of nonlinear equations to be solved for each value of the barrier parameter. Newton iterations give high quality solutions, but we are interested in modified Newton systems that are computationally less expensive at the expense of lower quality solutions. We propose a structured modified Newton approach where each modified Jacobian is composed of a previous Jacobian, plus one low-rank update matrix per succeeding iteration. Each update matrix is, for a given rank, chosen such that the distance to the Jacobian at the current iterate is minimized, in both 2-norm and Frobenius norm. The approach is structured in the sense that it preserves the nonzero pattern of the Jacobian. The choice of update matrix is supported by results in an ideal theoretical setting. We also produce numerical results with a basic interior-point implementation to investigate the practical performance within and beyond the theoretical framework. In order to improve performance beyond the theoretical framework, we also motivate and construct two heuristics to be added to the method.

Truss geometry and topology optimization with global stability constraints

In this paper, we introduce geometry optimization into an existing topology optimization workflow for truss structures with global stability constraints, assuming a linear buckling analysis. The design variables are the cross-sectional areas of the bars and the coordinates of the joints. This makes the optimization problem formulations highly nonlinear and yields nonconvex semidefinite programming problems, for which there are limited available numerical solvers compared with other classes of optimization problems. We present problem instances of truss geometry and topology optimization with global stability constraints solved using a standard primal-dual interior point implementation. During the solution process, both the cross-sectional areas of the bars and the coordinates of the joints are concurrently optimized. Additionally, we apply adaptive optimization techniques to allow the joints to navigate larger move limits and to improve the quality of the optimal designs.

An interior-point implementation developed and tuned for radiation therapy treatment planning

While interior-point methods share the same fundamentals, the implementation determines the actual performance. In order to attain the highest efficiency, different applications may require differently tuned implementations. In this paper we describe an implementation specifically designed for optimisation in radiation therapy. These problems are large-scale nonlinear (and sometimes nonconvex) constrained optimisation problems, consisting of both sparse and dense data. Several application-specific properties are exploited to enhance efficiency. Permuting, tiling and mixed precision arithmetic allow the algorithm to optimally process the mixed dense and sparse data matrices (making this step 2.2 times faster, and overall runtime reduction of 55%) and scalability (16 threads resulted in a speed-up factor of 9.8 compared to singlethreaded performance, against a speed-up factor of 7.7 for the less optimised implementation). Predefined cost-functions are hard-coded and the computationally expensive second derivatives are written in canonical form, and combined if multiple cost-functions are defined for the same clinical structure. The derivatives are then computed using a scaled matrix–matrix product. A cheap initialisation strategy based on the background knowledge reduces the number of iterations by 11%. We also propose a novel combined Mehrotra–Gondzio approach. The algorithm is extensively tested on a dataset consisting of 120 patients, distributed over 6 tumour sites/approaches. This test dataset is made publicly available.

A finite element interior-point implementation of tension field theory

This paper presents an interior-point implementation method of the relaxed strain energy function to model the stress distribution in membranes in the presence of wrinkles. The relaxed strain energy function is reformulated using the interior-point method. This formulation is proven to be quasi-convex with respect to the deformation gradient. A set of governing equations for the membrane is developed in the standard finite element format. The solution to this set of equations is obtained using the interior-point method, and several numerical examples are presented for validation. The proposed method is shown to converge and to be robust and efficient.

Solving security constrained optimal power flow problems by a structure exploiting interior point method

In this paper we present a new approach to solve the DC (n − 1) security constrained optimal power flow (SCOPF) problem by a structure exploiting interior point solver. Our approach is based on a reformulation of the linearised SCOPF model, in which most matrices that need to be factorized are constant. Hence, most factorizations and a large number of back-solve operations only need to be performed once. However, assembling the Schur complement matrix remains expensive in this scheme. To reduce the effort, we suggest using a preconditioned iterative method to solve the corresponding linear system. We suggest two main schemes to pick a good and robust preconditioner based on combining different “active” contingency scenarios of the SCOPF model. These new schemes are implemented within the object-oriented parallel solver (OOPS), a structure-exploiting primal-dual interior-point implementation. We give results on several SCOPF test problems. The largest example contains 500 buses. We compare the results from the original interior point method (IPM) implementation in OOPS and our new reformulation.

From global to local convergence of interior methods for nonlinear optimization

In this paper, we propose a modified primal–dual interior-point method for nonlinear programming that relaxes the requirement of closely following the central path and lends itself to dynamic updates of the barrier parameter. The latter promote better synchronization between the barrier parameter and the optimality residual and increase robustness. In the framework of a generic outer iteration, we show that the dynamic barrier parameter converges to zero, that the unit Newton step is asymptotically accepted and that linear or superlinear convergence occurs when the barrier parameter goes to zero linearly or superlinearly. A salient feature of our algorithm resides in the transparent transition between the global and local regimes. Where a typical implementation of an interior-point method consists in chaining a globalization algorithm with a fast locally convergent algorithm, the method that we propose remains one and the same in the global and local phases. Numerical experiments show that our method improves the standard interior-point implementation substantially by dramatically reducing the number of inner iterations per outer iteration and by controlling the barrier parameter dynamically. It also compares favourably with other recent and less recent heuristic dynamic updates.

Efficient Implementation of Linear Programming Decoding

While linear programming (LP) decoding provides more flexibility for finite-length performance analysis than iterative message-passing (IMP) decoding, it is computationally more complex to implement in its original form, due to both the large size of the relaxed LP problem and the inefficiency of using general-purpose LP solvers. This paper explores ideas for fast LP decoding of low-density parity-check (LDPC) codes. By modifying the previously reported Adaptive LP decoding scheme to allow removal of unnecessary constraints, we first prove that LP decoding can be performed by solving a number of LP problems that each contains at most one linear constraint derived from each of the parity-check constraints. By exploiting this property, we study a sparse interior-point implementation for solving this sequence of linear programs. Since the most complex part of each iteration of the interior-point algorithm is the solution of a (usually ill-conditioned) system of linear equations for finding the step direction, we propose a preconditioning algorithm to facilitate solving such systems iteratively. The proposed preconditioning algorithm is similar to the encoding procedure of LDPC codes, and we demonstrate its effectiveness via both analytical methods and computer simulation results.

Solving quadratically constrained convex optimization problems with an interior-point method

The paper describes the design of our interior-point implementation to solve large-scale quadratically constrained convex optimization problems. We outline the details of the implemented algorithm, which is based on the primal-dual interior-point method. Our discussion includes topics related to starting point strategies and to the implementation of the numerical algebra employed, with emphasis on sparsity and stability issues. Computational results are given on a demonstrative set of convex quadratically constrained quadratic problems.

Dynamic updates of the barrier parameter in primal-dual methods for nonlinear programming

We introduce a framework in which updating rules for the barrier parameter in primal-dual interior-point methods become dynamic. The original primal-dual system is augmented to incorporate explicitly an updating function. A Newton step for the augmented system gives a primal-dual Newton step and also a step in the barrier parameter. Based on local information and a line search, the decrease of the barrier parameter is automatically adjusted. We analyze local convergence properties, report numerical experiments on a standard collection of nonlinear problems and compare our results to a state-of-the-art interior-point implementation. In many instances, the adaptive algorithm reduces the number of iterations and of function evaluations. Its design guarantees a better fit between the magnitudes of the primal-dual residual and of the barrier parameter along the iterations.

A sparse signal reconstruction perspective for source localization with sensor arrays

We present a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposing penalties based on the /spl lscr//sub 1/-norm. A number of recent theoretical results on sparsifying properties of /spl lscr//sub 1/ penalties justify this choice. Explicitly enforcing the sparsity of the representation is motivated by a desire to obtain a sharp estimate of the spatial spectrum that exhibits super-resolution. We propose to use the singular value decomposition (SVD) of the data matrix to summarize multiple time or frequency samples. Our formulation leads to an optimization problem, which we solve efficiently in a second-order cone (SOC) programming framework by an interior point implementation. We propose a grid refinement method to mitigate the effects of limiting estimates to a grid of spatial locations and introduce an automatic selection criterion for the regularization parameter involved in our approach. We demonstrate the effectiveness of the method on simulated data by plots of spatial spectra and by comparing the estimator variance to the Crame/spl acute/r-Rao bound (CRB). We observe that our approach has a number of advantages over other source localization techniques, including increased resolution, improved robustness to noise, limitations in data quantity, and correlation of the sources, as well as not requiring an accurate initialization.

Application of an optimisation method for determining the reactive margin from voltage collapse in reactive power planning

Power system reactive power planning requires the computation of the reactive power margin from the point of voltage collapse in the steady-state in order to quantify the adequacy of the level of installed reactive power plant. This reactive margin is the difference between the maximum reactive load distributed across selected power system nodes and the reactive load at the planned system operating point. The margin can be estimated by applying an optimisation method using the total reactive load as the objective, with the load flow equations as equality constraints, whilst including system limits such as generator reactive capability limits as inequality constraints. This paper describes a full AC formulation of the optimisation problem. It is solved using an interior point implementation of the Newton method developed for the optimal power flow. This avoids potential difficulties in identifying the binding inequality constraints.