Mixed-integer nonlinear programming (MINLP) problems combine the numerical difficulties of handling nonlinear functions with the challenge of optimizing in the context of nonconvex …

These decision problems lead to mixed-integer nonlinear programming (MINLP) problems that combine the combinatorial difficulty of optimizing over discrete vari-able sets with the …

Mixed Integer Nonlinear Programming (MINLP) Mixed integer programming problems are defined as those where some or all of the decision variables are only allowed to be integers.

Jul 18, 2025 · This paper gives an overview of the development of Mixed-Integer Nonlinear Programming (MINLP) and Generalized Disjunctive Programming (GDP) over the past fifty years.

How to develop effective algorithms for nonlinear discrete/continuous? How to improve relaxation? How to solve nonconvex GDP problems to global optimality?

Oct 14, 2024 · Our experiments show that our methods efficiently solve MINLPs with up to tens of thousands of variables, providing high-quality solutions within milliseconds, even for problems …

Mixed Integer Nonlinear Programming (MINLP) refers to mathematical pro-gramming with continuous and discrete variables and nonlinearities in the ob-jective function and constraints.

May 30, 2025 · A novel method using decision diagrams to tackle complex MINLP challenges. Mixed-Integer Nonlinear Programming (MINLP) deals with optimization problems...

There are simpler ways than computing the Hessian to see whether a function is convex. For example, $f (x,y)=xy$ is definitively not convex, so you either have to fix $x_i$ and $y_ {ij}$ or …

Solving an MINLP can be roughly divided into two major tasks. What are they and how are they connected? Adding "nonlinear" to "mixed-integer" makes the problem even more di cult. How …