Projected Newton methods and optimization of multicommodity flows

Bibliography: p. 26-28."August 1981."Partial support provided by the National Science Foundation Grant ECS-79-20834 Defense Advanced Research Project Agency Grant ONR-N00014-75-C-1183by Dimitri P. Bertsekas and Eli M. Gafni

YAM2: Yet another library for the M2M_2 variables using sequential quadratic programming

The $M_2$ variables are devised to extend $M_{T2}$ by promoting transverse masses to Lorentz-invariant ones and making explicit use of on-shell mass relations. Unlike simple kinematic variables such as the invariant mass of visible particles, where the variable definitions directly provide how to calculate them, the calculation of the $M_2$ variables is undertaken by employing numerical algorithms. Essentially, the calculation of $M_2$ corresponds to solving a constrained minimization problem in mathematical optimization, and various numerical methods exist for the task. We find that the sequential quadratic programming method performs very well for the calculation of $M_2$, and its numerical performance is even better than the method implemented in the existing software package for $M_2$. As a consequence of our study, we have developed and released yet another software library, YAM2, for calculating the $M_2$ variables using several numerical algorithms.Comment: 1+22 pages, 5 figures; matches published version; fixed title page for inspire record; The library is distributed via https://github.com/cbpark/YAM

On the constant positive linear dependence condition and its application to SQP methods

2000-2001 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Superlinearly Convergent Quasi-Newton Algorithms for Nonlinearly Constrained Optimization Problems

A class of algorithms for nonlinearly constrained optimization problems is proposed. The subproblems of the algorithms are linearly constrained quadratic minimization problems which contain an updated estimate of the Hessian of the Lagrangian. Under suitable conditions and updating schemes local convergence and a super1inear rate of convergence are established. The convergence proofs require among other things twice differentiable objective and constraint functions, while the calculations use only first derivative data. Rapid convergence has been obtained in a number of test problems by using a program based on the algorithms proposed here

Otimização de Estruturas Treliçadas Geometricamente Não Lineares Submetidas a Carregamento Dinâmico.

Este trabalho trata da otimização de estruturas treliçadas com comportamento não linear geométrico submetidas a carregamento dinâmico. O problema de otimização formulado tem o objetivo de determinar a área da seção transversal das barras que minimiza a massa total da estrutura, impondo-se restrições sobre os deslocamentos nodais e as tensões axiais. Para resolvê-lo, foi desenvolvido um programa computacional na plataforma MATLAB®, utilizando os algoritmos do método dos Pontos Interiores e do método da Programação Quadrática Sequencial presentes no Optimization Toolbox. Foram incluídas rotinas para agrupamento de barras e para conversão da solução ótima obtida com uso de variáveis de projeto contínuas em valores comerciais de perfis tubulares. O elemento finito não linear de treliça espacial é descrito por uma formulação Lagrangeana atualizada. O procedimento de análise dinâmica não linear geométrica implementado combina o método de Newmark com iterações do tipo Newton-Raphson, sendo validado pela comparação com exemplos presentes na literatura. Exemplos de treliças planas e espaciais submetidas a diferentes tipos de carregamento dinâmico são resolvidos com a aplicação do programa computacional desenvolvido

Algorithms for constrained optimization

Imperial Users onl

Adjoint-based algorithms and numerical methods for sensitivity generation and optimization of large scale dynamic systems

This thesis presents advances in numerical methods for the solution of optimal control problems. In particular, the new ideas and methods presented in this thesis contribute to the research fields of structure-exploiting Newton-type methods for large scale nonlinear programming and sensitivity generation for IVPs for ordinary differential equations and differential algebraic equations. Based on these contributions, a new lifted adjoint-based partially reduced exact-Hessian SQP (L-PRSQP) method for nonlinear multistage constrained optimization problems with large scale differential algebraic process models is proposed. It is particularly well suited for optimization problems which involve many state variables in the dynamic process but only few degrees of freedom, i.e., controls, parameter or free initial values. This L-PRSQP method can be understood as an extension of the work of Schäfer to the case of exact-Hessian SQP methods, making use of directional forward/adjoint sensitivities of second order. It stands hence in the tradition of the direct multiple shooting approaches for differential algebraic equations of index 1 of Bock and co-workers. To the novelties that are presented in this thesis further belong - the generalization of the direct multiple shooting idea to structure-exploiting algorithms for NLPs with an internal chain structure of the problem functions, - an algorithmic trick that allows these so-called lifted methods to compute the condensed subproblems directly based on minor modifications to the user given problem functions and without further knowledge on the internal structure of the problem, - a lifted adjoint-based exact-Hessian SQP method that is shown to be equivalent to a full-space approach, but only has the complexity of an unlifted/single shooting approach per iteration, - new adjoint schemes for sensitivity generation based on Internal Numerical Differentiation (IND) for implicit LMMs using the example of Backward Differentiation Formulas (BDF), - the combination of univariate Taylor coefficient (TC) propagation and IND, resulting in IND-TC schemes which allow for the first time the efficient computation of directional forward and forward/adjoint sensitivities of arbitrary order, - a strategy to propagate directional sensitivities of arbitrary order across switching events in the integration, - a local error control strategy for sensitivities and a heuristic global error estimation strategy for IVP solutions in connection with IND schemes, - the software packages DAESOL-II and SolvIND, implementing the ideas related to IVP solution and sensitivity generation, as well as the software packages LiftOpt and DynamicLiftOpt that implement the lifted Newton-type methods for general NLP problems and the L-PRSQP method in the optimal control context, respectively. The performance of the presented approaches is demonstrated by the practical application of our codes to a series of numerical test problems and by comparison to the performance of alternative state-of-the-art approaches, if applicable. In particular, the new lifted adjoint-based partially reduced exact-Hessian SQP method allows the efficient and successful solution of a practical optimal control problem for a binary distillation column, for which the solution using a direct multiple shooting SQP method with an exact-Hessian would have been prohibitively expensive until now