Nonlinear Optimization

Proximal-like contraction methods for monotone variational inequalities in a unified framework

  • Bingsheng He
  • Lizhi Liao
  • Xiang Wang
    • Categories Complementarity and Variational Inequalities, Constrained Nonlinear Optimization, Convex Optimization Tags , ,

    Approximate proximal point algorithms (abbreviated as APPAs) are classical approaches for convex optimization problems and monotone variational inequalities. To solve the subproblems of these algorithms, the projection method takes the iteration in form of $u^{k+1} = P_{\Omega}[u^k-\alpha_k d^k]$. Interestingly, many of them can be paired such that $%\exists \tilde{u}^k, \tilde{u}^k = P_{\Omega}[u^k – \beta_kF(v^k)] = … Read more

    Proximal Methods for Nonlinear Programming: Double Regularization and Inexact Subproblems

  • Jonathan Eckstein
  • Paulo J. S. Silva
    • Categories Constrained Nonlinear Optimization, Convex Optimization Tags , ,

    This paper describes the first phase of a project attempting to construct an efficient general-purpose nonlinear optimizer using an augmented Lagrangian outer loop with a relative error criterion, and an inner loop employing a state-of-the art conjugate gradient solver. The outer loop can also employ double regularized proximal kernels, a fairly recent theoretical development that … Read more

    Nonlinear Stepsize Control, Trust Regions and Regularizations for Unconstrained Optimization

  • Philippe L. Toint
    • Categories Bound-constrained Optimization, Nonlinear Systems and Least-Squares, Unconstrained Optimization Tags , ,

    A general class of algorithms for unconstrained optimization is introduced, which subsumes the classical trust-region algorithm and two of its newer variants, as well as the cubic and quadratic regularization methods. A unified theory of global convergence to first-order critical points is then described for this class. An extension to projection-based trust-region algorithms for nonlinear … Read more

    A second derivative SQP method: theoretical issues

  • Nicholas I. M. Gould
  • Daniel P. Robinson
    • Categories Constrained Nonlinear Optimization Tags , , , ,

    Sequential quadratic programming (SQP) methods form a class of highly efficient algorithms for solving nonlinearly constrained optimization problems. Although second derivative information may often be calculated, there is little practical theory that justifies exact-Hessian SQP methods. In particular, the resulting quadratic programming (QP) subproblems are often nonconvex, and thus finding their global solutions may be … Read more

    Second-order analysis of optimal control problems with control and initial-final state constraints

  • Joseph Frédéric Bonnans
  • Nikolai Osmolovskii
    • Categories Systems governed by Differential Equations Optimization Tags , , , ,

    This paper provides an analysis of Pontryagine mimina satisfying a quadratic growth condition, for optimal control problems of ordinary differential equations with constraints on initial-final state, as well as control constraints satisfying the uniform positive linear independence condition. Citation Rapport de Recherche INRIA 6707, Oct. 2008. Article Download View Second-order analysis of optimal control problems … Read more

    Dynamic Evolution for Risk-Neutral Densities

  • Ana M. Monteiro
  • Reha H. Tütüncü
  • Luis Nunes Vicente
    • Categories Finance and Economics, Quadratic Programming, Semi-definite Programming Tags , , , ,

    Option price data is often used to infer risk-neutral densities for future prices of an underlying asset. Given the prices of a set of options on the same underlying asset with different strikes and maturities, we propose a nonparametric approach for estimating the evolution of the risk-neutral density in time. Our method uses bicubic splines … Read more

    Incorporating Minimum Frobenius Norm Models in Direct Search

  • Ana Luisa Custódio
  • Luis Nunes Vicente
  • Humberto Rocha
    • Categories Optimization Software Benchmark, Unconstrained Optimization Tags , , , , ,

    The goal of this paper is to show that the use of minimum Frobenius norm quadratic models can improve the performance of direct-search methods. The approach taken here is to maintain the structure of directional direct-search methods, organized around a search and a poll step, and to use the set of previously evaluated points generated … Read more

    Global Optimization of Non-Linear Systems of Equations by Simulating the Flight of a Projectile in the Conformational Space

  • Nicholas Harkiolakis
    • Categories Nonlinear Optimization, Optimization Software and Modeling Systems, Stochastic Approaches Tags , , , , ,

    A new heuristic optimization algorithm is presented based on an analogy with the physical phenomenon of a projectile launched in a conformational space under the influence of a gravitational force. Its implementation simplicity and the option to enhance it with local search methods make it ideal for the optimization of non-linear systems of equations. The … Read more

    A globally convergent primal-dual interior-point 3D filter method for nonlinear SDP

  • Zhongyi Liu
    • Categories Nonlinear Optimization, Semi-definite Programming Tags , , ,

    This paper proposes a primal-dual interior-point filter method for nonlinear semidefinite programming, which is the first multidimensional (three-dimensional) filter methods for interior-point methods, and of course for constrained optimization. A freshly new definition of filter entries is proposed, which is greatly different from those in all the current filter methods. A mixed norm is used … Read more

    Infeasibility Detection and SQP Methods for Nonlinear Optimization

  • Richard H. Byrd
  • Frank E. Curtis
  • Jorge Nocedal
    • Categories Nonlinear Optimization Tags , ,

    This paper addresses the need for nonlinear programming algorithms that provide fast local convergence guarantees regardless of whether a problem is feasible or infeasible. We present a sequential quadratic programming method derived from an exact penalty approach that adjusts the penalty parameter automatically, when appropriate, to emphasize feasibility over optimality. The superlinear convergence of such … Read more