IBM Stochastic Solutions

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IBM Stochastic Solutions

Introduction

Stochastic programming supports decision making under uncertainty. It is a methodology for bringing uncertain future scenarios into the traditional decision making framework of linear programming. Just as linear programming models the optimal allocation of constrained resources to meet known demands, stochastic programming models the allocation of today's resources to meet tomorrow's unknown demands in such a way that the user can explore the trade offs with respect to expected risks and rewards and make informed decisions.

Users seeking the greater modeling power and flexibility of stochastic programming come up against high hurdles: there is more input data to be managed, the optimization problems are very large, and the solution arrays are larger and more difficult to analyze.

IBM Stochastic Solutions offers new technology to help the user meet the demands of modeling stochastic programs. It has an easy-to-use solver that operates from the command line. It is the first commercial-grade optimization solution to implement the Stochastic Mathematical Programming System (SMPS) input format for multistage stochastic programs. The flexible decomposition solver is robust and fast, and with callable modules, the user has node-by-node access to data and solutions for efficient solution analysis.

The components of IBM Stochastic Solutions are a solver, a suite of callable modules for advanced development, and the User's Guide and Reference containing documentation, problem examples, and sample drivers.

The Stochastic Solution Stand-alone Solver

The stand-alone solver runs from the command line. With multiple switches and command line parameters, the user can solve a stochastic program in Stochastic Mathematical Programming System format with IBM Stochastic Solutions' powerful flexible decomposition solver. The solver can also be deployed to analyze the optimal distribution of the stochastic solution. The Reference Section contains many example problems and applications. Without compiling a single line of code, the user has complete access to all aspects of the most sophisticated modeling environment for stochastic programming!

The Stochastic Solutions Callable Modules

More advanced modeling development is supported by the IBM Stochastic Solutions Callable Modules. These are designed to facilitate the integration of IBM Stochastic Solutions into applications. The independently callable modules are accessed by constructing specialized driver programs. These functions support advanced model generation, control of solver operation through callbacks, and analysis of solution data. Input modules support in-core modeling of stochastic programs, user callbacks monitor solver progress, and data access modules retrieve the solution for use in application processing.

Guide, Reference and Examples

The Guide and Reference is divided into three parts. The User's Guide contains basic information to help users formulate, solve, and analyze stochastic programming problems. The Reference Section contains detailed information about calling sequences, data structures, and messages. The Examples contains example problems and several driver programs.

Readers seeking a fuller introduction to the ideas and techniques of stochastic programming should consult the recently published texts by Birge and Louveaux, and Kall and Wallace .

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Notes:

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