AIMMS Excel Add-In

The Excel Add-In User’s Guide provides a description of an example of the AIMMS Excel Add-In and a reference guide. 



The AIMMS SDK (Software Development Kit) extends the integration capabilities of your AIMMS applications. It allows you to integrate an AIMMS optimization model with your custom application through a Java, C# or C++ interface. Typically you would use the AIMMS SDK to start an AIMMS session (either locally or remote), assign data to AIMMS identifiers, run an AIMMS procedure to solve the AIMMS optimization model, and retrieve the (solution) data. Of course, this flow can be adjusted to your preference. For example, you can import data from databases directly into the AIMMS model by calling AIMMS procedures. The AIMMS SDK is backwards compatible and runs with all currently supported AIMMS versions. More details, as well as examples about the AIMMS SDK, can be found in the AIMMS SDK documentation.


AIMMS Open Solver Interface

The Open Solver Interface Reference Guide provides an introduction and a reference for the AIMMS Open Solver Interface.


AIMMS White Papers

A Nonlinear Presolve Algorithm in AIMMS
This paper describes the AIMMS presolve algorithm for nonlinear problems. This presolve algorithm uses standard techniques like removing singleton rows, deleting fixed variables and redundant constraints, and tightening variable bounds by using linear constraints. Our algorithm also uses the expression tree of nonlinear constraints to tighten variable bounds.

The AIMMS Outer Approximation Algorithm for MINLP (using GMP functionality)
This document describes how to use the GMP variant of the AIMMS Outer Approximation (AOA) algorithm for solving MINLP problems. We show how the AOA algorithm can be combined with the nonlinear presolver and the multi-start algorithm.

Solving convex MINLP problems with AIMMS
This document describes the Quesada and Grossman algorithm that is implemented in AIMMS to solve convex MINLP problems. We benchmark this algorithm against AOA which implements the classic outer approximation algorithm.