SAS/OR(R) 9.2 User's Guide: Mathematical Programming
Acknowledgments- What's New in SAS/OR 9.2
- Using This Book
Introduction to Optimization- The INTPOINT Procedure
- Overview: INTPOINT Procedure
- Introduction
- Syntax: INTPOINT Procedure
- Functional Summary
- Dictionary of Options
- PROC INTPOINT Statement
- CAPACITY Statement
- COEF Statement
- COLUMN Statement
- COST Statement
- DEMAND Statement
- HEADNODE Statement
- ID Statement
- LO Statement
- NAME Statement
- NODE Statement
- QUIT Statement
- RHS Statement
- ROW Statement
- RUN Statement
- SUPDEM Statement
- SUPPLY Statement
- TAILNODE Statement
- TYPE Statement
- VAR Statement
- Details: INTPOINT Procedure
- Input Data Sets
- Output Data Sets
- Converting Any PROC INTPOINT Format to an MPS-Format SAS Data Set
- Case Sensitivity
- Loop Arcs
- Multiple Arcs
- Flow and Value Bounds
- Tightening Bounds and Side Constraints
- Reasons for Infeasibility
- Missing S Supply and Missing D Demand Values
- Balancing Total Supply and Total Demand
- How to Make the Data Read of PROC INTPOINT More Efficient
- Stopping Criteria
- Macro Variable _ORINTPO
- Memory Limit
- Examples: INTPOINT Procedure
- Example 2.1: Production, Inventory, Distribution Problem
- Example 2.2: Altering Arc Data
- Example 2.3: Adding Side Constraints
- Example 2.4: Using Constraints and More Alteration to Arc Data
- Example 2.5: Nonarc Variables in the Side Constraints
- Example 2.6: Solving an LP Problem with Data in MPS Format
- Example 2.7: Converting to an MPS-Format SAS Data Set
- References
- The LP Procedure
- Overview: LP Procedure
- Getting Started: LP Procedure
- Syntax: LP Procedure
- Details: LP Procedure
- Missing Values
- Dense Data Input Format
- Sparse Data Input Format
- Converting Any PROC LP Format to an MPS-Format SAS Data Set
- Converting Standard MPS Format to Sparse Format
- The Reduced Costs, Dual Activities, and Current Tableau
- Macro Variable _ORLP_
- Pricing
- Scaling
- Preprocessing
- Integer Programming
- Sensitivity Analysis
- Range Analysis
- Parametric Programming
- Interactive Facilities
- Memory Management
- Output Data Sets
- Input Data Sets
- Displayed Output
- ODS Table and Variable Names
- Memory Limit
- Examples: LP Procedure
- Example 3.1: An Oil Blending Problem
- Example 3.2: A Sparse View of the Oil Blending Problem
- Example 3.3: Sensitivity Analysis: Changes in Objective Coefficients
- Example 3.4: Additional Sensitivity Analysis
- Example 3.5: Price Parametric Programming for the Oil Blending Problem
- Example 3.6: Special Ordered Sets and the Oil Blending Problem
- Example 3.7: Goal-Programming a Product Mix Problem
- Example 3.8: A Simple Integer Program
- Example 3.9: An Infeasible Problem
- Example 3.10: Restarting an Integer Program
- Example 3.11: Alternative Search of the Branch-and-Bound Tree
- Example 3.12: An Assignment Problem
- Example 3.13: A Scheduling Problem
- Example 3.14: A Multicommodity Transshipment Problem with Fixed Charges
- Example 3.15: Converting to an MPS-Format SAS Data Set
- References
- The NLP Procedure
- Overview: NLP Procedure
- Getting Started: NLP Procedure
- Syntax: NLP Procedure
- Functional Summary
- Dictionary of Options
- PROC NLP Statement
- ARRAY Statement
- BOUNDS Statement
- BY Statement
- CRPJAC Statement
- DECVAR Statement
- GRADIENT Statement
- HESSIAN Statement
- INCLUDE Statement
- JACNLC Statement
- JACOBIAN Statement
- LABEL Statement
- LINCON Statement
- MATRIX Statement
- MIN, MAX, and LSQ Statements
- MINQUAD and MAXQUAD Statements
- NLINCON Statement
- PROFILE Statement
- Program Statements
- Details: NLP Procedure
- Criteria for Optimality
- Optimization Algorithms
- Finite-Difference Approximations of Derivatives
- Hessian and CRP Jacobian Scaling
- Testing the Gradient Specification
- Termination Criteria
- Active Set Methods
- Feasible Starting Point
- Line-Search Methods
- Restricting the Step Length
- Computational Problems
- Covariance Matrix
- Input and Output Data Sets
- Displayed Output
- Missing Values
- Computational Resources
- Memory Limit
- Examples: NLP Procedure
- Example 4.1: Using the DATA= Option
- Example 4.2: Using the INQUAD= Option
- Example 4.3: Using the INEST=Option
- Example 4.4: Restarting an Optimization
- Example 4.5: Approximate Standard Errors
- Example 4.6: Maximum Likelihood Weibull Estimation
- Example 4.7: Simple Pooling Problem
- Example 4.8: Chemical Equilibrium
- Example 4.9: Minimize Total Delay in a Network
- References
- The NETFLOW Procedure
- Overview: NETFLOW Procedure
- Getting Started: NETFLOW Procedure
- Syntax: NETFLOW Procedure
- Functional Summary
- Dictionary of Options
- Interactivity
- PROC NETFLOW Statement
- CAPACITY Statement
- COEF Statement
- COLUMN Statement
- CONOPT Statement
- COST Statement
- DEMAND Statement
- HEADNODE Statement
- ID Statement
- LO Statement
- MULT Statement
- NAME Statement
- NODE Statement
- PIVOT Statement
- PRINT Statement
- QUIT Statement
- RESET Statement
- RHS Statement
- ROW Statement
- RUN Statement
- SAVE Statement
- SHOW Statement
- SUPDEM Statement
- SUPPLY Statement
- TAILNODE Statement
- TYPE Statement
- VAR Statement
- Details: NETFLOW Procedure
- Input Data Sets
- Output Data Sets
- Converting Any PROC NETFLOW Format to an MPS-Format SAS Data Set
- Case Sensitivity
- Loop Arcs
- Multiple Arcs
- Pricing Strategies
- Dual Variables, Reduced Costs, and Status
- The Working Basis Matrix
- Flow and Value Bounds
- Tightening Bounds and Side Constraints
- Reasons for Infeasibility
- Missing S Supply and Missing D Demand Values
- Balancing Total Supply and Total Demand
- Warm Starts
- How to Make the Data Read of PROC NETFLOW More Efficient
- Macro Variable _ORNETFL
- Memory Limit
- The Interior Point Algorithm: NETFLOW Procedure
- Generalized Networks: NETFLOW Procedure
- Using the New EXCESS= Option in Pure Networks: NETFLOW Procedure
- Using the New EXCESS= Option in Generalized Networks: NETFLOW Procedure
- Examples: NETFLOW Procedure
- Example 5.1: Shortest Path Problem
- Example 5.2: Minimum Cost Flow Problem
- Example 5.3: Using a Warm Start
- Example 5.4: Production, Inventory, Distribution Problem
- Example 5.5: Using an Unconstrained Solution Warm Start
- Example 5.6: Adding Side Constraints, Using a Warm Start
- Example 5.7: Using a Constrained Solution Warm Start
- Example 5.8: Nonarc Variables in the Side Constraints
- Example 5.9: Pure Networks: Using the EXCESS= Option
- Example 5.10: Maximum Flow Problem
- Example 5.11: Generalized Networks: Using the EXCESS= Option
- Example 5.12: Generalized Networks: Maximum Flow Problem
- Example 5.13: Machine Loading Problem
- Example 5.14: Generalized Networks: Distribution Problem
- Example 5.15: Converting to an MPS-Format SAS Data Set
- References
- The OPTMODEL Procedure
- Overview: OPTMODEL Procedure
- Getting Started: OPTMODEL Procedure
- OPTMODEL Modeling Language: Basic Concepts
- Syntax: OPTMODEL Procedure
- OPTMODEL Expression Extensions
- AND Aggregation Expression
- CARD Function
- CROSS Expression
- DIFF Expression
- IF-THEN/ELSE Expression
- IN Expression
- Index Set Expression
- INTER Expression
- INTER Aggregation Expression
- MAX Aggregation Expression
- MIN Aggregation Expression
- OR Aggregation Expression
- PROD Aggregation Expression
- Range Expression
- Set Constructor Expression
- Set Literal Expression
- SETOF Aggregation Expression
- SLICE Expression
- SUM Aggregation Expression
- SYMDIFF Expression
- Tuple Expression
- UNION Expression
- UNION Aggregation Expression
- WITHIN Expression
- Details: OPTMODEL Procedure
- Examples: OPTMODEL Procedure
- Rewriting NLP Models for PROC OPTMODEL
- References
- The Interior Point Nonlinear Programming Solver -- Experimental
- The Linear Programming Solver
- The Mixed Integer Linear Programming Solver
- The NLPC Nonlinear Optimization Solver
- The Unconstrained Nonlinear Programming Solver
- The Quadratic Programming Solver -- Experimental
- The Sequential Quadratic Programming Solver
- The MPS-Format SAS Data Set
- The OPTLP Procedure
- Overview: OPTLP Procedure
- Getting Started: OPTLP Procedure
- Syntax: OPTLP Procedure
- Details: OPTLP Procedure
- Examples: OPTLP Procedure
- Example 15.1: Oil Refinery Problem
- Example 15.2: Using the Interior Point Solver
- Example 15.3: The Diet Problem
- Example 15.4: Reoptimizing after Modifying the Objective Function
- Example 15.5: Reoptimizing after Modifying the Right-Hand Side
- Example 15.6: Reoptimizing after Adding a New Constraint
- Example 15.7: Finding an Irreducible Infeasible Set
- References
- The OPTMILP Procedure
- The OPTQP Procedure
Index here
| Product | Release |
|---|---|
| SAS/OR | 9.2 |
- Type
- Usage and Reference
- Copyright Date
- March 2008
- Last Updated
- 15Apr2008
| Previous | | | Next |
| Introduction to Optimization |
PROC OPTMODEL
PROC OPTMODEL provides a language for concisely modeling linear programming problems. The language allows a model to be expressed in a form that matches the mathematical formulation. Within OPTMODEL you can declare a model, pass it directly to various solvers, and review the solver result. You can also save an instance of a linear model in data set form for use by PROC OPTLP. For more details, refer to Chapter 6, "The OPTMODEL Procedure."
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