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Knowledge Base

SAS/OR

SAS/OR User's Guide: Mathematical Programming - Procedures

More about This Product Feedback

For the complete SAS/OR 12.1 User's Guide: Mathematical Programming, go to the SAS/OR product documentation page.

Procedures

  • The BOM Procedure
    Performs bill of material processing. [HTML]
  • The CLP Procedure
    A finite-domain constraint programming solver for constraint satisfaction problems (CSPs) with linear, logical, global, and scheduling constraints. [HTML]
  • The CPM Procedure
    Used for planning, controlling, and monitoring a project. [HTML]
  • The DTREE Procedure
    An interactive procedure for decision analysis, it interprets a decision problem represented in SAS data sets, finds the optimal decisions, and plots on a line printer or a graphics device the decision tree showing the optimal decisions. [HTML]
  • The GA Procedure
    Enables implementation of the basic genetic algorithm by default, and to employ other advanced techniques to handle constraints, accelerate convergence, and perform multiobjective optimizations. [HTML]
  • The GANTT Procedure
    Produces a Gantt chart, which is a graphical scheduling tool for the planning and control of a project. [HTML]
  • The NETDRAW Procedure
    Draws a network diagram of the activities in a project. [HTML]
  • The OPTLP Procedure
    Provides three methods of solving linear programs (LPs). [HTML]
  • The OPTMILP Procedure
    The OPTMILP procedure is a solver for general mixed integer linear programs (MILPs). [HTML]
  • The OPTMODEL Procedure
    Comprises the powerful OPTMODEL modeling language and state-of-the-art solvers for several classes of mathematical programming problems. [HTML]
  • The OPTNET Procedure
    Used to analyze relationships between entities. [HTML]
  • The OPTQP Procedure
    Solves quadratic programs—problems with quadratic objective function and a collection of linear constraints, including lower and/or upper bounds on the decision variables. [HTML]
  • The PM Procedure
    An interactive procedure that can be used for planning, controlling, and monitoring a project. [HTML]

Solvers
  • The Linear Programming (LP) Solver
    Provides a framework for specifying and solving linear programs (LPs). [HTML]
  • The Mixed Integer Linear Programming (MILP) Solver
    Provides a framework for specifying and solving mixed integer linear programs (MILPs). [HTML]
  • The Nonlinear Programming (NLP) Solver
    The sparse nonlinear programming (NLP) solver is a component of the OPTMODEL procedure that can solve optimization problems containing both nonlinear equality and inequality constraints. [HTML]
  • The Quadratic Programming (QP) Solver
    Provides a framework for specifying and solving quadratic programs. [HTML]

Legacy Procedures and Solvers
  • The INTPOINT Procedure
    Solves the Network Program with Side Constraints (NPSC) problem and the more general Linear Programming (LP) problem. [HTML]
  • The LP Procedure
    Solves linear programs, integer programs, and mixed-integer programs. It also performs parametric programming, range analysis, and reports on solution sensitivity to changes in the right-hand-side constants and price coefficients. [HTML]
  • The Interior Point NLP (IPNLP) Solver
    Can solve nonlinear programming (NLP) problems that contain both nonlinear equality and inequality constraints. [HTML]
  • The NETFLOW Procedure
    Accepts the network specification in a format that is particularly suited to networks. [HTML]
  • The NLP Procedure
    Offers a set of optimization techniques for minimizing or maximizing a continuous nonlinear function f(x) of n decision variables, x = (x1.....xn)T with lower and upper bound, linear and nonlinear, equality and inequality constraints. [HTML]
  • The NLPC Nonlinear Optimization (NLPC) Solver
    Solves unconstrained nonlinear optimization problems and problems with a nonlinear objective function subject to bound, linear, or nonlinear constraints. It provides several optimization techniques that effectively handle these classes of problems. [HTML]
  • The Unconstrained Nonlinear Programming (NLPU) Solver
    Used for solving general unconstrained nonlinear programming (NLP) problems. [HTML]
  • The Sequential Quadratic Programming (SQP) Solver
    The sequential quadratic programming (SQP) solver is a component of the OPTMODEL procedure, and it can be used for solving general nonlinear programming (NLP) problems. [HTML]


More about This Product Feedback

For the complete SAS/OR 9.3 User's Guide: Mathematical Programming, go to the SAS/OR product documentation page.

Procedures

  • The BOM Procedure
    Performs bill of material processing. [HTML]
  • The CLP Procedure
    A finite-domain constraint programming solver for constraint satisfaction problems (CSPs) with linear, logical, global, and scheduling constraints. [HTML]
  • The CPM Procedure
    Used for planning, controlling, and monitoring a project. [HTML]
  • The DTREE Procedure
    An interactive procedure for decision analysis, it interprets a decision problem represented in SAS data sets, finds the optimal decisions, and plots on a line printer or a graphics device the decision tree showing the optimal decisions. [HTML]
  • The GA Procedure
    Enables implementation of the basic genetic algorithm by default, and to employ other advanced techniques to handle constraints, accelerate convergence, and perform multiobjective optimizations. [HTML]
  • The GANTT Procedure
    Produces a Gantt chart, which is a graphical scheduling tool for the planning and control of a project. [HTML]
  • The NETDRAW Procedure
    Draws a network diagram of the activities in a project. [HTML]
  • The OPTLP Procedure
    Provides three methods of solving linear programs (LPs). [HTML]
  • The OPTMILP Procedure
    The OPTMILP procedure is a solver for general mixed integer linear programs (MILPs). [HTML]
  • The OPTMODEL Procedure
    Comprises the powerful OPTMODEL modeling language and state-of-the-art solvers for several classes of mathematical programming problems. [HTML]
  • The OPTQP Procedure
    Solves quadratic programs—problems with quadratic objective function and a collection of linear constraints, including lower and/or upper bounds on the decision variables. [HTML]
  • The PM Procedure
    An interactive procedure that can be used for planning, controlling, and monitoring a project. [HTML]

Solvers
  • The Linear Programming (LP) Solver
    Provides a framework for specifying and solving linear programs (LPs). [HTML]
  • The Mixed Integer Linear Programming (MILP) Solver
    Provides a framework for specifying and solving mixed integer linear programs (MILPs). [HTML]
  • The Nonlinear Programming (NLP) Solver
    The sparse nonlinear programming (NLP) solver is a component of the OPTMODEL procedure that can solve optimization problems containing both nonlinear equality and inequality constraints. [HTML]
  • The Quadratic Programming (QP) Solver
    Provides a framework for specifying and solving quadratic programs. [HTML]

Legacy Procedures and Solvers
  • The INTPOINT Procedure
    Solves the Network Program with Side Constraints (NPSC) problem and the more general Linear Programming (LP) problem. [HTML]
  • The LP Procedure
    Solves linear programs, integer programs, and mixed-integer programs. It also performs parametric programming, range analysis, and reports on solution sensitivity to changes in the right-hand-side constants and price coefficients. [HTML]
  • The Interior Point NLP (IPNLP) Solver
    Can solve nonlinear programming (NLP) problems that contain both nonlinear equality and inequality constraints. [HTML]
  • The NETFLOW Procedure
    Accepts the network specification in a format that is particularly suited to networks. [HTML]
  • The NLP Procedure
    Offers a set of optimization techniques for minimizing or maximizing a continuous nonlinear function f(x) of n decision variables, x = (x1.....xn)T with lower and upper bound, linear and nonlinear, equality and inequality constraints. [HTML]
  • The NLPC Nonlinear Optimization (NLPC) Solver
    Solves unconstrained nonlinear optimization problems and problems with a nonlinear objective function subject to bound, linear, or nonlinear constraints. It provides several optimization techniques that effectively handle these classes of problems. [HTML]
  • The Unconstrained Nonlinear Programming (NLPU) Solver
    Used for solving general unconstrained nonlinear programming (NLP) problems. [HTML]
  • The Sequential Quadratic Programming (SQP) Solver
    The sequential quadratic programming (SQP) solver is a component of the OPTMODEL procedure, and it can be used for solving general nonlinear programming (NLP) problems. [HTML]