Convexification for Non-Convex Mixed-Integer Quadratic Programming
1.6 هزار بار بازدید -
3 سال پیش
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2021 Virtual INFORMS Optimization Society
2021 Virtual INFORMS Optimization Society Conference
Wednesday, March 31, 11am-12noon EDT
Speaker: Sam Burer, University of Iowa
Convexification is an important technique used for solving non-convex mixed-integer quadratic programs. We discuss three recent convexification results for nonconvex quadratic programming over: (i) bounded (x1,x2,x3) with x1*x2 = x3; (ii) continuous (x1,x2) and binary (y1,y2) such that (0,0) less than or equal to (x1,x2) less than or equal to (y1,y2); and (iii) a ball intersected with a second-order cone. Although these structures may seem quite specialized, they appear as critical substructures in numerous applications. In addition to describing these three results, we survey the landscape---and the current research frontier---of convexification techniques in this area.
This talk reflects joint work with Kurt Anstreicher, Anders Eltved, and Kyungchan Park.
Wednesday, March 31, 11am-12noon EDT
Speaker: Sam Burer, University of Iowa
Convexification is an important technique used for solving non-convex mixed-integer quadratic programs. We discuss three recent convexification results for nonconvex quadratic programming over: (i) bounded (x1,x2,x3) with x1*x2 = x3; (ii) continuous (x1,x2) and binary (y1,y2) such that (0,0) less than or equal to (x1,x2) less than or equal to (y1,y2); and (iii) a ball intersected with a second-order cone. Although these structures may seem quite specialized, they appear as critical substructures in numerous applications. In addition to describing these three results, we survey the landscape---and the current research frontier---of convexification techniques in this area.
This talk reflects joint work with Kurt Anstreicher, Anders Eltved, and Kyungchan Park.
3 سال پیش
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