A nonlinear integer programming problem in general is NP-hard. If the objective function has some special form (e.g., convexity), the Lagrangian relaxation [1] or dynamic programming [2] can be used. If the objective function has a concave or log-concave property, the steepest ascent algorithm [3] can be used.
References
[1] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge Univ. Press, 2004.
[2] T. Ibaraki and N. Katoh, Resource Allocation Problems: Algorithmic Approaches. The MIT Press, 1988.
[3] G. P. Akilov, L. V. Kantorovich, Functional Analysis, 2nd edition. Pergamon Press, 1982.
Showing posts with label Nonlinear Integer Programming. Show all posts
Showing posts with label Nonlinear Integer Programming. Show all posts
Monday, September 5, 2016
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