Abstract We propose a novel Mixed-Integer Nonlinear Programming (MINLP) model for sparse optimization based on the polyhedral k-norm. put special emphasis application of in Feature Selection Support Vector Machine (SVM) classification. address continuous relaxation problem, which comes out form fractional programming problem (FPP). In particular, we consider possible way tackling FPP by reformu...

Mixed-integer optimization problems arise in various application fields, such as chemical engineering and the optimization of medical image processing pipelines. Stochastic optimization algorithms, such as evolution strategies and estimation of distribution algorithms, can be used as solution methods for solving these problems approximately. Especially for real-world problems they often prove t...

Two binary integer programming discrete-time models and two precedence-based mixed integer programming continuous-time formulations are developed for the resource-constrained project scheduling problem. The discrete-time models are based on the definition of binary variables that describe the processing state of every activity between two consecutive time points, while the continuous-time model...

We study the complexity of computing the mixed-integer hull conv(P ∩ Z ×R) of a polyhedron P . Given an inequality description, with one integer variable, the mixed-integer hull can have exponentially many vertices and facets in d. For n, d fixed, we give an algorithm to find the mixed integer hull in polynomial time. Given P = conv(V ) and n fixed, we compute a vertex description of the mixed-...

A Unifying Framework for the Convexification of Mixed-Integer Conic Binary Sets The paper “Conic Mixed-Binary Sets: Convex Hull Characterizations and Applications,” by Fatma Kilinc-Karzan, Simge Kucukyavuz, Dabeen Lee, Soroosh Shafieezadeh-Abadeh, develops a unifying framework convexifying mixed-integer conic binary sets. Many applications in machine-learning operations research give rise to in...

Recently the concept of facility efficiency, which defined by data envelopment analysis (DEA), introduced as a location modeling objective, that provides facilities location’s effect on their performance in serving demands. By combining the DEA models with the location problem, two types of “efficiencies” are optimized: spatial efficiency which measured by finding the least cost location and al...

data envelopment analysis (dea) is a technique used to compare efficiency in various sectors such as hospitals, chain stores, and dealerships. it represents a set of linear programming techniques and uses deter-ministic data (inputs and outputs), in stable conditions. the dea technique cannot be used when there is data with indeterministic nature, or when there is an environment with dynamic co...

background and aims: diabetes type ii (non-insulin dependent) which is one of the most prevalent diabetes types in the world emerges in people with the age of above 55 and genetic and environmental factors interfere in this disease. the aim of this study was to determine the factors affecting diabetes type ii with generalized mixed linear model. methods: population of this study included 2820 p...