Strengthening Convex Relaxations with Bound Tightening for Power Network Optimization

  title={Strengthening Convex Relaxations with Bound Tightening for Power Network Optimization},
  author={Carleton Coffrin and Hassan L. Hijazi and Pascal Van Hentenryck},
Convexification is a fundamental technique in (mixed-integer) nonlinear optimization and many convex relaxations are parametrized by variable bounds, i.e., the tighter the bounds, the stronger the relaxations. This paper studies how bound tightening can improve convex relaxations for power network optimization. It adapts traditional constraintprogramming concepts (e.g., minimal network and bound consistency) to a relaxation framework and shows how bound tightening can dramatically improve power… CONTINUE READING
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