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The Natural Language for Optimization (NL4Opt) Competition was created to investigate methods of extracting the meaning and formulation of an optimization problem based on its text description. Specifically, the goal of the competition is to increase the accessibility and usability of optimization solvers by allowing non-experts to interface with them using natural language. We separate this challenging goal into two sub-tasks: (1) recognize and label the semantic entities that correspond to the components of the optimization problem; (2) generate a meaning representation (i.e., a logical form) of the problem from its detected problem entities. The first task aims to reduce ambiguity by detecting and tagging the entities of the optimization problems. The second task creates an intermediate representation of the linear programming (LP) problem that is converted into a format that can be used by commercial solvers. In this report, we present the LP word problem dataset and shared tasks for the NeurIPS 2022 competition. Furthermore, we investigate and compare the performance of the ChatGPT large language model against the winning solutions. Through this competition, we hope to bring interest towards the development of novel machine learning applications and datasets for optimization modeling.

In this work, we consider two-stage quadratic optimization problems under ellipsoidal uncertainty. In the first stage, one needs to decide upon the values of a subset of optimization variables (control variables). In the second stage, the uncertainty is revealed and the rest of the optimization variables (state variables) are set up as a solution to a known system of possibly non-linear equations. This type of problem occurs, for instance, in optimization for dynamical systems, such as electric power systems as well as gas and water networks. We propose a convergent iterative algorithm to build a sequence of approximately robustly feasible solutions with an improving objective value. At each iteration, the algorithm optimizes over a subset of the feasible set and uses affine approximations of the second-stage equations while preserving the non-linearity of other constraints. We implement our approach and demonstrate its performance on Matpower instances. This paper focuses on quadratic problems, but the approach is suitable for more general setups., Comment: 26 pages, 3 tables, 1 figure

Women, visible minorities, and other socially disadvantaged groups continue to be underrepresented in STEM education. Understanding students' motivations for pursuing a STEM major, and the roles gender, nationality, parental education attainment, and socio-economic background play in shaping students' motivations can support the design of more effective recruitment efforts towards these groups. In this paper, we propose and develop a novel text mining approach incorporating the Latent Dirichlet Allocation and word embeddings to analyze applicants' motivational factors for choosing an engineering program. We apply the proposed method to a dataset of 43,645 applications to the engineering school of a large Canadian university. We then investigate the relationship between applicants' gender, nationality, and family income and educational attainment, and their stated motivations for applying to their engineering program of choice. We find that interest in technology and the desire to make social impact are the two most powerful motivators for applicants. Additionally, while we find significant motivational differences related to applicants' nationality and family socio-economic status, gender has the strongest and the most robust impact on students' motivations for studying engineering.