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Mixed-integer nonlinear programming with binary variables
2022Operational problems involving discrete structures naturally arise in a vast number of applications which provide the impetus for the development of mixed-integer programming (MIP). In this dissertation, we are mainly concerned with the theory of mixed-integer nonlinear programming (MINLP) with indicator variables as well as their applications in other
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Mixed-Integer Nonlinear Programming Problems (MINLPs)
1999Mixed-integer problems are those that involve both continuous and integer variables. The introduction of integer variables allows the modeling of complex decisions through graph theoretic representations denoted as superstructures (Floudas, 1995). This representation leads to the simultaneous determination of the optimal structure of a network and its ...
Christodoulos A. Floudas +8 more
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Tighter relaxations in mixed-integer nonlinear programming
2020Mixed-integer nonlinear programming (MINLP) is one of the most important classes of mathematical optimization problems that combines difficulties from mixed-integer linear programming and nonlinear programming, namely optimizing over a set that is described by integrality, linear, and nonlinear restrictions.
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Mixed integer programming approaches for nonlinear and stochastic programming.
2009In this thesis we study how to solve some nonconvex optimization problems by using methods that capitalize on the success of Linear Programming (LP) based solvers for Mixed Integer Linear Programming (MILP). A common aspect of our solution approaches is the use, development and analysis of small but strong extended LP/MILP formulations and ...
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MINLPLib—A Collection of Test Models for Mixed-Integer Nonlinear Programming
INFORMS Journal on Computing, 2003Michael R Bussieck
exaly