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The essential features of the branch-and-bound approach to constrained optimization are described, and several specific applications are reviewed. These include integer linear programming (Land-Doig and Balas methods), nonlinear programming (minimization of nonconvex objective functions), the traveling-salesman problem (Eastman and Little, et al. methods), and the quadratic assignment problem (Gilmore and Lawler methods). Computational considerations, including trade-offs between length of computation and storage requirements, are discussed and a comparison with dynamic programming is made. Various applications outside the domain of mathematical programming are also mentioned.
Lawler et al. (Mon,) studied this question.
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