Shade the region above this line, since the second inequality isn't satisfied there. Lastly, plot the line 3x1+2x2 = 18. Shade the region where inequality 3 is not satisfied. You should now have a ...

Discover a new heuristic for linearizing convex quadratic programming problems. Explore the use of Karush-Kuhn-Tucker conditions and a linear objective function. Overcome unboundedness challenges with ...

Discover how to locate multiple facilities in convex sets with fuzzy parameters. Our paper presents a linear programming model using block norms, rectilinear, and infinity norms. Optimize the sum of ...

This chapter helps the students to identify linear and quadratic optimization problems. It utilizes Python and the module CvxPy, as a modeling language for convex optimization problems. The chapter ...

Convexity is a key property to global optimal for mathematical programming. Previous convex fitting works can only guarantee convexity at table entries or sampled points using semi-definite ...

The sharp identification region of θ, denoted Θ I, can then be obtained as the set of minimizers of the distance from a properly specified vector of moments of random variables to this Aumann ...