Wolfe duality

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In mathematical optimization, Wolfe duality, named after Philip Wolfe, is type of dual problem in which the objective function and constraints are all differentiable functions. Using this concept a lower bound for a minimization problem can be found because of the weak duality principle. [1]

Contents

Mathematical formulation

For a minimization problem with inequality constraints,

the Lagrangian dual problem is

where the objective function is the Lagrange dual function. Provided that the functions and are convex and continuously differentiable, the infimum occurs where the gradient is equal to zero. The problem

is called the Wolfe dual problem. [2] [ clarification needed ] This problem employs the KKT conditions as a constraint. Also, the equality constraint is nonlinear in general, so the Wolfe dual problem may be a nonconvex optimization problem. In any case, weak duality holds. [3]

See also

References

  1. Philip Wolfe (1961). "A duality theorem for non-linear programming". Quarterly of Applied Mathematics. 19 (3): 239–244. doi: 10.1090/qam/135625 .
  2. Eiselt, Horst A. (2019). Nonlinear Optimization: Methods and Applications. International Series in Operations Research and Management Science Ser. Carl-Louis Sandblom. Cham: Springer International Publishing AG. p. 147. ISBN   978-3-030-19462-8.
  3. Geoffrion, Arthur M. (1971). "Duality in Nonlinear Programming: A Simplified Applications-Oriented Development". SIAM Review. 13 (1): 1–37. doi:10.1137/1013001. JSTOR   2028848.