Mixed-Integer Nonlinear Optimization. Belotti, P., Kirches, C., Leyffer, S., Linderoth, J., & Luedtke, J. doi abstract bibtex Many optimal decision problems in scientific, engineering, and public sector applications involve both discrete decisions and nonlinear system dynamics that affect the quality of the final design or plan. These decision problems lead to mixed-integer nonlinear programming (MINLP) problems that combine the combinatorial difficulty of optimizing over discrete variable sets with the challenges of handling nonlinear functions. We review models and applications of MINLP, and survey the state of the art in methods for solving this challenging class of problems.
@article{belotti_mixed-integer_nodate,
title = {Mixed-{Integer} {Nonlinear} {Optimization}},
doi = {10.1017/s0962492913000032},
abstract = {Many optimal decision problems in scientific, engineering, and public sector applications involve both discrete decisions and nonlinear system dynamics that affect the quality of the final design or plan. These decision problems lead to mixed-integer nonlinear programming (MINLP) problems that combine the combinatorial difficulty of optimizing over discrete variable sets with the challenges of handling nonlinear functions. We review models and applications of MINLP, and survey the state of the art in methods for solving this challenging class of problems.},
language = {en},
author = {Belotti, Pietro and Kirches, Christian and Leyffer, Sven and Linderoth, Jeff and Luedtke, Jim},
keywords = {/unread},
pages = {123},
}
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