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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