Quadratic Programming

Sensitivity analysis in convex quadratic optimization: simultaneous perturbation of the objective and right-hand-side vectors

  • Alireza Ghaffari Hadigheh
  • Oleksandr Romanko
  • Tamás Terlaky
    • Categories Finance and Economics, Linear Programming, Quadratic Programming Tags , , , , , ,

    In this paper we study the behavior of Convex Quadratic Optimization problems when variation occurs simultaneously in the right-hand side vector of the constraints and in the coefficient vector of the linear term in the objective function. It is proven that the optimal value function is piecewise-quadratic. The concepts of transition point and invariancy interval … Read more

    Computational experience with an interior point algorithm for large scale contact problems

  • Dominikus Noll
  • Y. Renard
  • G. Tanoh
    • Categories Constrained Nonlinear Optimization, Mechanical Engineering, Quadratic Programming Tags , , ,

    In this paper we present an interior point method for large scale Signorini elastic contact problems. We study the case of an elastic body in frictionless contact with a rigid foundation. Primal and primal-dual algorithms are developed to solve the quadratic optimization problem arising in the variational formulation. Our computational study confirms the efficiency of … Read more

    A New Conjugate Gradient Algorithm Incorporating Adaptive Ellipsoid Preconditioning

  • Renato D.C. Monteiro
  • Arkadi Nemirovski
  • Jerome W. O'Neal
    • Categories Convex Optimization, Linear, Cone and Semidefinite Programming, Quadratic Programming Tags , , , , , ,

    The conjugate gradient (CG) algorithm is well-known to have excellent theoretical properties for solving linear systems of equations $Ax = b$ where the $n\times n$ matrix $A$ is symmetric positive definite. However, for extremely ill-conditioned matrices the CG algorithm performs poorly in practice. In this paper, we discuss an adaptive preconditioning procedure which improves the … Read more

    Newton-KKT Interior-Point Methods for Indefinite Quadratic Programming

  • Pierre-Antoine Absil
  • André L. Tits
    • Categories Constrained Nonlinear Optimization, Quadratic Programming Tags , , ,

    Two interior-point algorithms are proposed and analyzed, for the (local) solution of (possibly) indefinite quadratic programming problems. They are of the Newton-KKT variety in that (much like in the case of primal-dual algorithms for linear programming) search directions for the `primal´ variables and the Karush-Kuhn-Tucker (KKT) multiplier estimates are components of the Newton (or quasi-Newton) … Read more

    SENSITIVITY ANALYSIS IN CONVEX QUADRATIC OPTIMIZATION: INVARIANT SUPPORT SET INTERVAL

  • Alireza Ghaffari Hadigheh
  • Tamás Terlaky
    • Categories Quadratic Programming

    In sensitivity analysis one wants to know how the problem and the optimal solutions change under the variation of the input data. We consider the case when variation happens in the right hand side of the constraints and/or in the linear term of the objective function. We are interested to find the range of the … Read more

    Recovering Risk-Neutral Probability Density Functions from Options Prices using Cubic Splines

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

    We present a new approach to estimate the risk-neutral probability density function (pdf) of the future prices of an underlying asset from the prices of options written on the asset. The estimation is carried out in the space of cubic spline functions, yielding appropriate smoothness. The resulting optimization problem, used to invert the data and … Read more

    Constrained optimization in seismic reflection tomography: an SQP augmented Lagrangian approach

  • Frederic Delbos
  • Jean Charles Gilbert
  • R. Glowinski
  • D. Sinoquet
    • Categories Applications - Science and Engineering, Constrained Nonlinear Optimization, Quadratic Programming Tags , , , , , ,

    Seismic reflection tomography is a method for determining a subsurface velocity model from the traveltimes of seismic waves reflecting on geological interfaces. From an optimization viewpoint, the problem consists in minimizing a nonlinear least-squares function measuring the mismatch between observed traveltimes and those calculated by ray tracing in this model. The introduction of a priori … Read more

    An Iterative Solver-Based Infeasible Primal-Dual Path-Following Algorithm for Convex QP

  • Zhaosong Lu
  • Renato D.C. Monteiro
  • Jerome W. O'Neal
    • Categories Linear, Cone and Semidefinite Programming, Quadratic Programming Tags , , ,

    In this paper we develop an interior-point primal-dual long-step path-following algorithm for convex quadratic programming (CQP) whose search directions are computed by means of an iterative (linear system) solver. We propose a new linear system, which we refer to as the \emph{augmented normal equation} (ANE), to determine the primal-dual search directions. Since the condition number … Read more

    Global linear convergence of an augmented Lagrangian algorithm for solving convex quadratic optimization problems

  • Frederic Delbos
  • Jean Charles Gilbert
    • Categories Quadratic Programming Tags , , , , , , ,

    We consider an augmented Lagrangian algorithm for minimizing a convex quadratic function subject to linear inequality constraints. Linear optimization is an important particular instance of this problem. We show that, provided the augmentation parameter is large enough, the constraint value converges {\em globally\/} linearly to zero. This property is viewed as a consequence of the … Read more

    Quadratic interior-point methods in statistical disclosure control

  • Jordi Castro
    • Categories Network Optimization, Quadratic Programming, Statistics

    The safe dissemination of statistical tabular data is one of the main concerns of National Statistical Institutes (NSIs). Although each cell of the tables is made up of the aggregated information of several individuals, the statistical confidentiality can be violated. NSIs must guarantee that no individual information can be derived from the released tables. One … Read more