A Sequential Quadratic Optimization Algorithm with Rapid Infeasibility Detection
نویسندگان
چکیده
We present a sequential quadratic optimization (SQO) algorithm for nonlinear constrained optimization. The method attains all of the strong global and fast local convergence guarantees of classical SQO methods, but has the important additional feature that fast local convergence is guaranteed when the algorithm is employed to solve infeasible instances. A two-phase strategy, carefully constructed parameter updates, and a line search are employed to promote such convergence. The first phase subproblem determines the highest level of improvement in linearized feasibility that can be attained locally. The second phase subproblem then seeks optimality in such a way that the resulting search direction attains a level of improvement in linearized feasibility that is proportional to that attained in the first phase. The subproblem formulations and parameter updates ensure that near an optimal solution, the algorithm reduces to a classical SQO method for optimization, and near an infeasible stationary point, the algorithm reduces to a (perturbed) SQO method for minimizing constraint violation. Global and local convergence guarantees for the algorithm are proved under common assumptions and numerical results are presented for a large set of test problems.
منابع مشابه
Infeasibility Detection and SQP Methods for Nonlinear Optimization
This paper addresses the need for nonlinear programming algorithms that provide fast local convergence guarantees no matter if a problem is feasible or infeasible. We present an active-set sequential quadratic programming method derived from an exact penalty approach that adjusts the penalty parameter appropriately to emphasize optimality over feasibility, or vice versa. Conditions are presente...
متن کاملA TRUST-REGION SEQUENTIAL QUADRATIC PROGRAMMING WITH NEW SIMPLE FILTER AS AN EFFICIENT AND ROBUST FIRST-ORDER RELIABILITY METHOD
The real-world applications addressing the nonlinear functions of multiple variables could be implicitly assessed through structural reliability analysis. This study establishes an efficient algorithm for resolving highly nonlinear structural reliability problems. To this end, first a numerical nonlinear optimization algorithm with a new simple filter is defined to locate and estimate the most ...
متن کاملSNOPT: An SQP Algorithm for Large-Scale Constrained Optimization
Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first derivatives are available and that the constraint gradients are sparse. We discuss an SQP algorithm th...
متن کاملSNOPT : An SQP Algorithm for Large - Scale Constrained Optimization ∗ Philip
Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first derivatives are available and that the constraint gradients are sparse. Second derivatives are assumed...
متن کاملAn inexact restoration strategy for the globalization of the sSQP method
An algorithm to solve equality constrained optimization problems based on stabilized sequential quadratic programming, augmented Lagrangian and inexact restoration methods is presented. This formulation has attractive features in the sense that no constraint qualifications are needed at the limit point, and that it overcomes ill-conditioning of the subproblems when the penalty parameter is larg...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- SIAM Journal on Optimization
دوره 24 شماره
صفحات -
تاریخ انتشار 2014