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When to use sequential quadratic programming?
Sequential quadratic programming (SQP) is an iterative method for constrained nonlinear optimization. SQP methods are used on mathematical problems for which the objective function and the constraints are twice continuously differentiable.
What does SQP mean?
SQP
| Acronym | Definition |
|---|---|
| SQP | Software Quality Plan |
| SQP | Strategic Quality Plan |
| SQP | Software Quality Program |
| SQP | Survey Quality Predictor (software) |
What is full form of SQP?
When to use sequential quadratic programming for optimization?
Sequential quadratic programming ( SQP) is an iterative method for constrained nonlinear optimization. SQP methods are used on mathematical problems for which the objective function and the constraints are twice continuously differentiable .
How are SQP methods used to solve optimization problems?
SQP methods solve a sequence of optimization subproblems, each of which optimizes a quadratic model of the objective subject to a linearization of the constraints. If the problem is unconstrained, then the method reduces to Newton’s method for finding a point where the gradient of the objective vanishes.
Which is the best SQP package for solving the quadratic subproblem?
In addition to fmincon, SNOPT and FILTERSQP are two other commercial SQP packages, and each uses a different non-linear method to solve the quadratic subproblem. [1] Line search methods and trust-region methods are trusted options for this step, and sub-gradient methods have also been proposed.
Which is the best commercial quadratic programming package?
Commercial SQP packages include checks for the feasibility of the sub-problem in order to account for rank deficiencies. In addition to fmincon, SNOPT and FILTERSQP are two other commercial SQP packages, and each uses a different non-linear method to solve the quadratic subproblem. [1]