This is the most critical step. Define your variables clearly with units and bounds.
Uncertainty has always been present, but classical stochastic programming requires knowing probability distributions. Today’s hot methodology uses data-driven robust optimization (DDRO). modelling in mathematical programming methodol hot
Given a document-term matrix $X \in \mathbbR^m \times n$ (where $m$ is the vocabulary size and $n$ is the number of documents), topic modeling seeks matrices: This is the most critical step
Where $k \ll m$ is the number of topics. The general optimization problem is: Modelling pattern: Reformulate as an extensive form with
$$ \min_W, H \frac12 | X - WH |_F^2 $$
Subject to constraints ensuring interpretability (e.g., non-negativity).