A tag is a keyword or label that categorizes your question with other, similar questions. Using the right tags makes it easier for others to find and answer your question.
For questions involving mathematical problems that aim to minimize or maximize some objective function, possibly subject to one or more constraints.
For questions about mathematical optimization problems involving both continuous and binary or general integer variables.
For questions related to problems that optimize (i.e., minimize or maximize) a linear objective subject to linear constraints.
For questions related to the process of converting a real-world problem into a mathematical model. Can include questions related to linearization, logical constraints, tightness of formulations, and s…
For questions related to software designed to solve very general classes of optimization problems to guaranteed optimality.
For questions about mathematical optimization problems involving binary or general integer variables.
For questions asking for references from the literature or textbooks on specific topics.
Questions related to the commercial solver IBM ILOG CPLEX Optimization Studio.
For questions related to constraints, i.e. any restriction or relation a set of decision variables has to satisfy.
For questions about solving Operations Research problems using the Python programming language.
For questions related to techniques for converting nonlinear expressions in optimization models into equivalent (or approximately equivalent) linear ones.
For questions on modeling satisfaction or optimization problems in languages designed for expressing (often high-level) constraints on decision variables.
For questions about mathematical optimization problems involving a nonlinear objective function and/or nonlinear constraints.
For questions about optimization over a discrete solution space.
For questions about methods that assign resources to work requests (e.g. processor time to tasks) usually minimizing some cost measure (e.g. makespan).
Questions related to the commercial solver Gurobi Optimizer.
For questions related to the design or implementation of algorithms (exact or heuristic) for solving optimization problems.
Questions related to or-tools, Google's open-source software suite for optimization
For questions related to convex functions and convex sets, especially as they relate to optimization problems.
For questions that involve variables than can only take on one of two values, usually 0 or 1.
For questions related to optimization problems for routing vehicles through a set of nodes, especially the vehicle routing problem (VRP) and its variants.
For questions related to the Python optimization modeling package Pyomo.
For questions about the theoretical runtime needed for solving computational problems, often measured in the size of the input. This includes questions about whether polynomial time algorithms exist, …
For questions asking for real-world applications of specific concepts.
For questions on quadratic programming, methods to solve them and related solvers. Use this tag along with (optimization).
For questions about constraints that can be expressed in (usually propositional) logic.
For questions about solving Operations Research problems using the C++ programming language.
For questions related to the use of a "big M" (large constant) in a mathematical modeling context, either in the objective function (to initialize the simplex method) or in constraints (to formulate l…
Questions related to resources (e.g. data, results, code) that can be downloaded freely from the Internet.
For questions related to the traveling salesman problem (TSP), which seeks, for a given set of nodes, the shortest path that visits every node and returns to the starting node.
For questions about mathematical models and algorithms for optimizing inventories.
For questions related to graphs, a mathematical object consisting of a set of nodes with edges connecting certain pairs of them.
For questions related to the simplex method for linear programming (LP), which solves LPs optimally by moving iteratively from in corner point of the feasible region to a better one.
For questions on duals of (primal) mathematical programs that optimize the complementary bound. When minimizing, for example, primal solutions are upper bounds, and dual solutions lower bounds on the …
For questions about non-convex optimization problems where the objective or any of the constraints are non-convex.
For questions related to algorithms expected to produce good solutions, with no guarantee of optimality, in relatively short computation time.