Providing software solutions since 1976
The Interior Point Nonlinear Programming Solver
The IPNLP solver in the OPTMODEL procedure enables you to solve general nonlinear programming problems. Mathematically, a general nonlinear programming problem can be stated as
where 
is the nonlinear objective function and
is the set of general nonlinear equality and
inequality constraints. The lower and upper bounds on the decision variable are denoted by l
and u, respectively.
Solution Techniques
The IPNLP solver implements a primal-dual interior point algorithm that includes several powerful features from recent state-of-the-art algorithms. It uses a line-search procedure and a merit function to ensure convergence of the iterates to a local minimum.
Key Features
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The IPNLP solver iteratively generates better approximations of the primal variables and the dual variables. The solver also ensures that the primal iterates always remain strictly within their bounds for every iteration.
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The IPNLP solver is particularly suited for problems that contain many dense nonlinear inequality constraints, and it is expected to outperform solvers that use other NLP solution techniques.
For further details, see the SAS/OR® User's Guide: Mathematical Programming: The Interior Point
NLP Solver.
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Examples
- Solving Highly Nonlinear Optimization Problems
- Solving Unconstrained Optimization Problems
- Solving NLP Problems with Range Constraints
- Solving Large-Scale NLP Problems
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