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

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.

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

A branch of mathematical programming where solutions are constrained to be integer values, often solved using Branch and Bound methods.

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.

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

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

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