Embedding robust and eecient algorithms in high-quality reusable software is an eeective means of transferring the expertise of mathematicians to researchers in other disciplines, and a wealth of such software is already available. Unfortunately, its volume and distribution over labyrinthine computer networks makes it diicult for users to locate appropriate tools. The Guide to Available Mathema...

GAMS (General Algebraic Modelling System) is a computer language which was originally developed to assist economists at the World Bank in the quantitative analysis of economic policy questions (Meeraus (1983), Brooke, Kendrick and Meeraus (1987)). At the time of its conception and initial development, linear programming (LP) was the standard framework in which market equilibrium models were con...

This document illustrates the Dantzig-Wolfe decomposition algorithm using GAMS.

The LGO solver system integrates a suite of efficient global and local scope optimization strategies. LGO can handle complex nonlinear models under ‘minimal’ (continuity) assumptions. The LGO implementation linked to the GAMS modeling environment has several new features, and improved overall functionality. In this article we review the solver options and the usage of GAMS/LGO. We also present ...

Propensity score matching is often used in observational studies to create treatment and control groups with similar distributions of observed covariates. Typically, propensity scores are estimated using logistic regressions that assume linearity between the logistic link and the predictors. We evaluate the use of generalized additive models (GAMs) for estimating propensity scores. We compare l...

This document describes an implementation of a Column Generation algorithm using GAMS. The well-known cutting stock problem is used as an example.

Generalized additive models (GAMs) are a generalization of generalized linear models (GLMs) and constitute a powerful technique which has successfully proven its ability to capture nonlinear relationships between explanatory variables and a response variable in many domains. In this paper, GAMs are proposed as base classifiers for ensemble learning. Three alternative ensemble strategies for bin...

The Lipschitz Global Optimizer (LGO) software integrates global and local scope search methods, to handle nonlinear optimization models. This paper introduces the LGO implementation linked to the General Algebraic Modeling System (GAMS). First we review the key features and basic usage of the GAMS /LGO solver option, then present reproducible numerical results to illustrate its performance.

An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs /GAMs modeling: from species distribution to environmental management , held in Riedera...