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An optimization algorithm where the solution space is divided (branching) and evaluated (bounding) to find the optimum solution by excluding suboptimal areas.

The process of dividing the feasible solution space into smaller subspaces by adding additional constraints.

Calculating the upper or lower bounds of objective function values within a subspace to determine if it can contain the optimal solution.

A point in the solution space representing a subproblem with its own constraints and objective function value.

The initial node representing the original problem before any branching has taken place.

The process of eliminating subspaces (or nodes) that cannot possibly contain an optimal solution based on the current bounds.

The smallest possible value of the objective function within a subspace, used to determine if further branching is necessary.

The highest possible value of the objective function within a subspace, used to determine if further branching is necessary.

A smaller instance of the original problem, derived from branching at a node and inheriting constraints from the parent node.

The process of determining whether a subproblem obeys all the problem's constraints and therefore presents a valid solution.

A strategy to choose the next node for branching by selecting the one with the best bound, which indicates the most potential for improvement.