Parallel solver for trajectory optimization search directions

A key algorithmic element of a real-time trajectory optimization hardware/software implementation is presented, the search step solver. This is one piece of an algorithm whose overall goal is to make nonlinear trajectory optimization fast enough to provide real-time commands during guidance of a vehicle such as an aeromaneuvering orbiter or the National Aerospace Plane. Many methods of nonlinear programming require the solution of a quadratic program (QP) at each iteration to determine the search step. In the trajectory optimization case, the QP has a special dynamic programming structure, an LQR-like structure. The algorithm exploits this special structure with a divide-and-conquer type of parallel implementation. A hypercube message-passing parallel machine, the INTEL iPSC/2, has been used. The algorithm solves a (p·N)-stage problem onN processors inO(p + log₂ N) operations. The algorithm yields a factor of 8 speed-up over the fastest known serial algorithm when solving a 1024-stage test problem on 32 processors.This research was supported in part by the National Aeronautics and Space Administration under Grant No. NAG-1-1009.     

An interior point method for quadratic programs based on conjugate projected gradients

We propose an interior point method for large-scale convex quadratic programming where no assumptions are made about the sparsity structure of the quadratic coefficient matrixQ. The interior point method we describe is a doubly iterative algorithm that invokes aconjugate projected gradient procedure to obtain the search direction. The effect is thatQ appears in a conjugate direction routine rather than in a matrix factorization. By doing this, the matrices to be factored have the same nonzero structure as those in linear programming. Further, one variant of this method istheoretically convergent with onlyone matrix factorization throughout the procedure.     

A parallel variable-metric dynamic programming algorithm

This paper presents a potentially parallel iterative algorithm for the solution of the unconstrainedN-stage decision problem of dynamic programming. The basis of the algorithm is the use of variable-metric minimization techniques to develop a quadratic approximation to the cost function at each stage. The algorithm is applied to various problems, and comparisons with other algorithms are made.This research forms part of the author's PhD program, and is supported by the Department of Scientific and Industrial Research of the New Zealand Government. The author is indebted to Dr. B. A. Murtagh, PhD supervisor, for his encouragement and support during the preparation of this paper.     

A Structured Reduced Sequential Quadratic Programming and Its Application to a Shape Design Problem

The objective of this work is to solve a model one dimensional duct design problem using a particular optimization method. The design problem is formulated as an equality constrained optimization, called all at once method, so that the analysis problem is not solved until the optimal design is reached. Furthermore, the sparsity structure in the Jacobian of the linearized constraints is exploited by decomposing the variables into the design and flow parts. To achieve this, sequential quadratic programming with BFGS update for the reduced Hessian of the Lagrangian function is used with the variable reduction method which preserves the structure of the Jacobian in representing the null space basis matrix. By updating the reduced Hessians of which the dimension is the number of design variables, the storage requirement for the Hessians is reduced by a large amount. In addition, the flow part of the Jacobian can be computed analytically.The algorithm with a line search globalization is described. A global and local analysis is provided with a modification of the paper by Byrd and Nocedal [Mathematical Programming 49(1991) pp 285-323] in which they analyzed a similar algorithm with the orthogonal factorization method which assumes the orthogonality of the null space basis matrix. Numerical results are obtained and compared favorably with results from the black box method, unconstrained optimization formulation.     

A robust sequential quadratic programming method

The sequential quadratic programming method developed by Wilson, Han and Powell may fail if the quadratic programming subproblems become infeasible, or if the associated sequence of search directions is unbounded. This paper considers techniques which circumvent these difficulties by modifying the structure of the constraint region in the quadratic programming subproblems. Furthermore, questions concerning the occurrence of an unbounded sequence of multipliers and problem feasibility are also addressed.Work supported in part by the National Science Foundation under Grant No. DMS-8602399 and by the Air Force Office of Scientific Research under Grant No. ISSA-860080.Work supported in part by the National Science Foundation under Grant No. DMS-8602419.     

Parallel algorithms for nonlinear programming problems

This paper describes several parallel algorithms for solving nonlinear programming problems. Two approaches where parallelism can successfully be introduced have been explored: a quadratic approximation method based on penalty function and a dual method. These methods are improved by using two algorithms originally proposed for solving unconstrained problems: the parallel variable metric algorithm and the parallel Jacobson-Oksman algorithm. Even though general problems are dealt with, particular emphasis is placed on the potential of these parallel methods for separable programming problems. The numerical effectiveness of the algorithms is demonstrated on a set of test problems using a Cray-1S vector computer and serial computers (with respect to sequential versions of the same methods).These studies were sponsored in part by the CERT. The author would particularly like to thank Ph. Berger (LSI-ENSEEIHT), the researchers of the DERI (CERT) and of the Groupe Structures, Aerospatiale, for their assistance.     

A fast algorithm for equality-constrained quadratic programming on the alliant FX/8

An efficient implementation of the null-space method for quadratic programming on the Alliant FX/8 computer is described. The most computationally significant operations in this method are the orthogonal factorization of the constraint matrix and corresponding similarity transformation of the Hessian, and the Cholesky factorization of the reduced Hessian matrix. It is shown how these can be implemented in such a way as to take full advantage of the Alliant's parallel/vector capabilities and memory hierarchy. Timing results are given on a set of test problems for which the data can be easily accommodated in core memory. Note: Research partially supported by the Air Force office of Scientific Research under grant AFOSR-ISSA-870092 and the National Science Foundation under grant DMS-8619903.     

Globally solving box-constrained nonconvex quadratic programs with semidefinite-based finite branch-and-bound

We consider a recent branch-and-bound algorithm of the authors for nonconvex quadratic programming. The algorithm is characterized by its use of semidefinite relaxations within a finite branching scheme. In this paper, we specialize the algorithm to the box-constrained case and study its implementation, which is shown to be a state-of-the-art method for globally solving box-constrained nonconvex quadratic programs. S. Burer was supported in part by NSF Grants CCR-0203426 and CCF-0545514.     

A quadratic programming algorithm

By using conjugate directions a method for solving convex quadratic programming problems is developed. The algorithm generates a sequence of feasible solutions and terminates after a finite number of iterations. Extensions of the algorithm for nonconvex and large structured quadratic programming problems are discussed.Sponsored by the United States Army under Contract No. DAAG29-80-C-0041 and in part by the Natural Sciences and Engineering Research Council of Canada under Grant Nos. A 8189 and E 5582.     

Efficient Sequential Quadratic Programming Implementations for Equality-Constrained Discrete-Time Optimal Control

Efficient sequential quadratic programming (SQP) implementations are presented for equality-constrained, discrete-time, optimal control problems. The algorithm developed calculates the search direction for the equality-based variant of SQP and is applicable to problems with either fixed or free final time. Problem solutions are obtained by solving iteratively a series of constrained quadratic programs. The number of mathematical operations required for each iteration is proportional to the number of discrete times N. This is contrasted by conventional methods in which this number is proportional to N ³. The algorithm results in quadratic convergence of the iterates under the same conditions as those for SQP and simplifies to an existing dynamic programming approach when there are no constraints and the final time is fixed. A simple test problem and two application problems are presented. The application examples include a satellite dynamics problem and a set of brachistochrone problems involving viscous friction.     

A shortest path algorithm in robotics and its implementation on the FPS T-20 hypercube

A broad class of problems involving the optimal control of robot arms can be formulated as dynamic programming problems whose structure is particularly attractive for parallel processing. For certain simple cost functions the dynamic programming formulation reduces to determining the shortest path through a network. This algorithm has been implemented on a Floating Point Systems' T-20 hypercube computer. An analysis of the performance of the algorithm provides several important insights into the interplay between problem size and the number of processors in a parallel computer. The results also underscore the potential for parallel computers in real-time control applications.This work was supported in part by the Office of Naval Research, Contract N 00014-86-K-0693.     

Interior-point methods for nonconvex nonlinear programming: orderings and higher-order methods

The paper extends prior work by the authors on loqo, an interior point algorithm for nonconvex nonlinear programming. The specific topics covered include primal versus dual orderings and higher order methods, which attempt to use each factorization of the Hessian matrix more than once to improve computational efficiency. Results show that unlike linear and convex quadratic programming, higher order corrections to the central trajectory are not useful for nonconvex nonlinear programming, but that a variant of Mehrotra’s predictor-corrector algorithm can definitely improve performance. Received: May 3, 1999 / Accepted: January 24, 2000?Published online March 15, 2000     

Dynamic factorization in large-scale optimization

Factorization of linear programming (LP) models enables a large portion of the LP tableau to be represented implicitly and generated from the remaining explicit part. Dynamic factorization admits algebraic elements which change in dimension during the course of solution. A unifying mathematical framework for dynamic row factorization is presented with three algorithms which derive from different LP model row structures: generalized upper bound rows, pure network rows, and generalized network rows. Each of these structures is a generalization of its predecessors, and each corresponding algorithm exhibits just enough additional richness to accommodate the structure at hand within the unified framework. Implementation and computational results are presented for a variety of real-world models. These results suggest that each of these algorithms is superior to the traditional, non-factorized approach, with the degree of improvement depending upon the size and quality of the row factorization identified.Corresponding author.     

一类带有MaxEMin评判的补偿型随机规划算法

补偿型随机规划一般假定随机变量的概率分布具有完备信息, 但实际情况往往只能获得部分信息. 针对离散概率具有一类线性部分信息条件而建立了带有MaxEMin评判的两阶段随机规划模型, 借助二次规划和对偶分解方法得到了可行性切割和最优切割, 给出了基于L-型的求解算法, 并证明了算法的收敛性. 通过数值实验表明了算法的有效性.     

Interior-Point Solver for Large-Scale Quadratic Programming Problems with Bound Constraints

In this paper, we present an interior-point algorithm for large and sparse convex quadratic programming problems with bound constraints. The algorithm is based on the potential reduction method and the use of iterative techniques to solve the linear system arising at each iteration. The global convergence properties of the potential reduction method are reassessed in order to take into account the inexact solution of the inner system. We describe the iterative solver, based on the conjugate gradient method with a limited-memory incomplete Cholesky factorization as preconditioner. Furthermore, we discuss some adaptive strategies for the fill-in and accuracy requirements that we use in solving the linear systems in order to avoid unnecessary inner iterations when the iterates are far from the solution. Finally, we present the results of numerical experiments carried out to verify the effectiveness of the proposed strategies. We consider randomly generated sparse problems without a special structure. Also, we compare the proposed algorithm with the MOSEK solver. Research partially supported by the MIUR FIRB Project RBNE01WBBB “Large-Scale Nonlinear Optimization.”     

Dynamic Programming Method for Constrained Discrete-Time Optimal Control

A dynamic programming method is presented for solving constrained, discrete-time, optimal control problems. The method is based on an efficient algorithm for solving the subproblems of sequential quadratic programming. By using an interior-point method to accommodate inequality constraints, a modification of an existing algorithm for equality constrained problems can be used iteratively to solve the subproblems. Two test problems and two application problems are presented. The application examples include a rest-to-rest maneuver of a flexible structure and a constrained brachistochrone problem.     

A stable method for solving certain constrained least squares problems

This paper presents a feasible descent algorithm for solving certain constrained least squares problems. These problems are specially structured quadratic programming problems with positive semidefinite Hessian matrices that are allowed to be singular. The algorithm generates a finite sequence of subproblems that are solved using the numerically stable technique of orthogonal factorization with reorthogonalization and Given's transformation updating.This material is based upon work supported by the National Science Foundation under Grant No. MCS 78-06716 and by the International Institute for Applied Systems Analysis.     

On quadratic andO\left( {\sqrt {nL} } \right) convergence of a predictor—corrector algorithm for LCP

Recently several new results have been developed for the asymptotic (local) convergence of polynomial-time interior-point algorithms. It has been shown that the predictor—corrector algorithm for linear programming (LP) exhibits asymptotic quadratic convergence of the primal—dual gap to zero, without any assumptions concerning nondegeneracy, or the convergence of the iteration sequence. In this paper we prove a similar result for the monotone linear complementarity problem (LCP), assuming only that a strictly complementary solution exists. We also show by example that the existence of a strictly complementarity solution appears to be necessary to achieve superlinear convergence for the algorithm.Research supported in part by NSF Grants DDM-8922636 and DDM-9207347, and an Interdisciplinary Research Grant of the University of Iowa, Iowa Center for Advanced Studies.     

一类大规模二次规划问题的可行下降分解算法

本文对一类大规模二次规划问题，提出了矩阵剖分的概念和方法，并将问题转化为求解一系列容易求解的小规模二次规划子问题．另外，通过施加某些约束机制，使子问题所产生的迭代点均为可行下降点．在通常的假定下，证明算法具有全局收敛性，大量数值实验表明，本文所提出的新算法是有效的。     

An SQP algorithm for extended linear-quadratic problems in stochastic programming

Extended Linear-Quadratic Programming (ELQP) problems were introduced by Rockafellar and Wets for various models in stochastic programming and multistage optimization. Several numerical methods with linear convergence rates have been developed for solving fully quadratic ELQP problems, where the primal and dual coefficient matrices are positive definite. We present a two-stage sequential quadratic programming (SQP) method for solving ELQP problems arising in stochastic programming. The first stage algorithm realizes global convergence and the second stage algorithm realizes superlinear local convergence under a condition calledB-regularity.B-regularity is milder than the fully quadratic condition; the primal coefficient matrix need not be positive definite. Numerical tests are given to demonstrate the efficiency of the algorithm. Solution properties of the ELQP problem underB-regularity are also discussed.Supported by the Australian Research Council.