Fangwei Li Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, China [email protected] Xinyue Zhang Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, China [email protected] Jiang Zhu Chongqing Key Lab of Mobile Communications Technology, Chongqing Unive...

Image restoration models based on total variation (TV) have become popular since their introduction by Rudin, Osher, and Fatemi (ROF) in 1992. The dual formulation of this model has a quadratic objective with separable constraints, making projections onto the feasible set easy to compute. This paper proposes application of gradient projection (GP) algorithms to the dual formulation. We test var...

Sequential quadratic programming (SQP) methods solve nonlinear optimization problems by finding an approximate solution of a sequence of quadratic programming (QP) subproblems. Each subproblem involves the minimization of a quadratic model of the objective function subject to the linearized constraints. Depending on the definition of the quadratic model, the QP subproblem may be nonconvex, lead...

An appealing feature of interior methods for linear programming is that the number of iterations required to solve a problem tends to be relatively insensitive to the choice of initial point. This feature has the drawback that it is difficult to design interior methods that efficiently utilize information from an optimal solution to a “nearby” problem. We discuss this feature in the context of ...

We propose using SQP (Sequential Quadratic Programming) to directly recover 3D quadratic surface parameters from multiple views. A surface equation is used as a constraint. In addition to the sum of squared reprojection errors defined in the traditional bundle adjustment, a Lagrangian term is added to force recovered points to satisfy the constraint. The minimization is realized by SQP. Our alg...

This paper discusses an Interval Quadratic Programming (IQP) problem, where the constraints coefficients and the right-hand sides are represented by interval data. First, the focus is on a common method for solving Interval Linear Programming problem. Then the idea is extended to the IQP problem. Based on this method each IQP problem is reduced to two classical Quadratic Programming (QP) proble...

We consider a combined IPM-SQP method to solve smooth nonlinear optimization problems, which may possess a large number of variables and a sparse Jacobian matrix of the constraints. Basically, the algorithm is a sequential quadratic programming (SQP) method, where the quadratic programming subproblem is solved by a primal-dual interior point method (IPM). A special feature of the algorithm is t...

This paper presents a general and systematic methodology, termed complete simulation-based sequential quadratic programming (CSB-SQP), for determining the optimal control of building HVAC&R systems. This approach allows the coupling of a detailed simulation program with an efficient optimization method, namely the sequential quadratic programming (SQP) algorithm. This approach allows the use of...

A minimum-time Model Predictive Control (MPC) problem is considered. By employing a time scaling transformation and cost regularization, it is shown that this problem becomes amenable to the application of parametric Integrated Perturbation Analysis Sequential Quadratic Programming (IPA-SQP). The IPA-SQP framework exploits neighboring extremal optimal control and sequential quadratic programmin...

The problem of analysing the worst-case performance of a nonlinear aeroelastic system is formulated as an optimisation problem and solved using gradient-based local optimisation methods. Two different approaches are considered. The first formulates the optimisation problem in the classical Euler-Lagrange setting and computes the gradient by backward integration of the resulting adjoint system. ...