The depth-first and breadth-first backtracking algorithms
described in the preceding sections both
naıvely traverse the entire solution space.
However, sometimes we can determine that a given node
in the solution space does not lead to the optimal solution--either because the given solution and all its successors are infeasible
or because we have already found a solution that is guaranteed to be
better than any successor of the given solution.
In such cases,
the given node and its successors need not be considered.
In effect,
we can *prune* the solution tree,
thereby reducing the number of solutions to be considered.

For example, consider the scales balancing problem
described in Section .
Consider a partial solution in which
we have placed *k* weights onto the pans ( ) and,
therefore, *n*-*k* weights remain to be placed.
The difference between the weights of the left and right pans is given by

and the sum of the weights still to be placed is

Suppose that .
I.e., the total weight remaining is less than the difference between
the weights in the two pans.
Then, the best possible solution that we can obtain
without changing the positions of the weights that have already been placed is
The quantity is a *lower bound*
on the value of the objective function for all the solutions
in the solution tree below the given partial solution .

In general, during the traversal of the solution space we may
have already found a complete, feasible solution for which
the objective function is *less* than .
In that case,
there is no point in considering any of the solutions below .
I.e., we can *prune* the subtree rooted at node
from the solution tree.
A backtracking algorithm that prunes the search space in this manner
is called a *branch-and-bound* algorithm.

Copyright © 1997 by Bruno R. Preiss, P.Eng. All rights reserved.