Call for papers on MINLP — Special issue of Optimization and Engineering 
(OPTE)

Mathematical models for optimal decisions often require both nonlinear 
and discrete components. The first mixed-integer nonlinear optimization 
(MINLP) algorithms and software were designed by application engineers. 
While these efforts initially proved useful, scientists, engineers, and 
practitioners have realized that a transformational shift in technology 
will be required for MINLP to achieve its full potential. Current 
obstacles include characterizing the computability boundary, effectively 
exploiting known optimization technologies for specialized classes of 
MINLP, and effectively integrating logic and learning holistically 
throughout algorithms.

By combining theory and implementation, this special issue will 
contribute toward energizing efforts making MINLP as ubiquitous a 
paradigm for both modeling and solving important decision problems as 
mixed-integer linear programming and nonlinear programming have become 
in recent years. In particular, we plan to highlight: MINLP solver 
software, intersecting mixed-integer and nonlinear programming, 
complexity and convergence analysis, convexification techniques, and 
driving applications.

OPTE welcomes contributions encompassing all mathematical optimization 
methods and algorithms relevant to engineering. Given the potential 
impact of MINLP technology across all areas of science and engineering, 
this special issue welcomes submissions in all areas of MINLP, including 
those without a direct engineering component.

The deadline for submissions is 25 May 2018. Please submit to 
Optimization and Engineering at 
http://www.springer.com/mathematics/journal/11081 and select special 
issue SI: MINLP 2018.

Nick Sahinidis
John E. Swearingen Professor at Carnegie Mellon University
Editor-in-Chief Optimization and Engineering
http://archimedes.cheme.cmu.edu
http://www.springer.com/mathematics/journal/11081