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  2. Weak duality - Wikipedia

    en.wikipedia.org/wiki/Weak_duality

    In applied mathematics, weak duality is a concept in optimization which states that the duality gap is always greater than or equal to 0. This means that for any minimization problem, called the primal problem, the solution to the primal problem is always greater than or equal to the solution to the dual maximization problem.

  3. Duality (optimization) - Wikipedia

    en.wikipedia.org/wiki/Duality_(optimization)

    The duality gap is zero if and only if strong duality holds. Otherwise the gap is strictly positive and weak duality holds. [5] In computational optimization, another "duality gap" is often reported, which is the difference in value between any dual solution and the value of a feasible but suboptimal iterate for the primal problem.

  4. Perturbation function - Wikipedia

    en.wikipedia.org/wiki/Perturbation_function

    The duality gap is the difference of the right and left hand side of the inequality (,) (,),where is the convex conjugate in both variables. [3] [4]For any choice of perturbation function F weak duality holds.

  5. Duality gap - Wikipedia

    en.wikipedia.org/wiki/Duality_gap

    In optimization problems in applied mathematics, the duality gap is the difference between the primal and dual solutions. If d ∗ {\displaystyle d^{*}} is the optimal dual value and p ∗ {\displaystyle p^{*}} is the optimal primal value then the duality gap is equal to p ∗ − d ∗ {\displaystyle p^{*}-d^{*}} .

  6. Dual linear program - Wikipedia

    en.wikipedia.org/wiki/Dual_linear_program

    The weak duality theorem says that, for each feasible solution x of the primal and each feasible solution y of the dual: c T x ≤ b T y. In other words, the objective value in each feasible solution of the dual is an upper-bound on the objective value of the primal, and objective value in each feasible solution of the primal is a lower-bound ...

  7. Wolfe duality - Wikipedia

    en.wikipedia.org/wiki/Wolfe_duality

    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.

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