斯莱特 Slater's condition

Slater's condition

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In mathematics, Slater's condition (or Slater condition) is a sufficient condition for strong duality to hold for a convex optimization problem. This is a specific example of a constraint qualification. In particular, if Slater's condition holds for the primal problem, then the duality gap is 0, and if the dual value is finite then it is attained.[1]

[edit]Mathematics

Given the problem

\text{Minimize }\; f_0(x)
\text{subject to: }\
f_i(x) \le 0 , i = 1,\ldots,m
Ax = b

with f_0,\ldots,f_m convex (and therefore a convex optimization problem). Then strong duality holds if there exists an x \in \operatorname{relint}(D) (where relint is the relative interior and D = \cap_{i = 0}^m \operatorname{dom}(f_i)) such that

f_i(x) < 0, i = 1,\ldots,m and
Ax = b.\, [2]

If the first k constraints, f_1,\ldots,f_k are linear functions, then strong duality holds if there exists an x \in \operatorname{relint}(D) such that

f_i(x) \le 0, i = 1,\ldots,k,
f_i(x) < 0, i = k+1,\ldots,m, and
Ax = b.\, [2]

[edit]Generalized Inequalities

Given the problem

\text{Minimize }\; f_0(x)
\text{subject to: }\
f_i(x) \le_{K_i} 0 , i = 1,\ldots,m
Ax = b

where f_0 is convex and f_i is K_i-convex for each i. Then Slater's condition says that if there exists an x \in \operatorname{relint}(D) such that

f_i(x) <_{K_i} 0, i = 1,\ldots,m and
Ax = b

then strong duality holds.[2]

[edit]References

  1. ^ Borwein, Jonathan; Lewis, Adrian (2006). Convex Analysis and Nonlinear Optimization: Theory and Examples (2 ed.). Springer. ISBN 978-0-387-29570-1.
  2. a b c Boyd, Stephen; Vandenberghe, Lieven (2004) (pdf). Convex Optimization. Cambridge University Press. ISBN 978-0-521-83378-3. Retrieved October 3, 2011.

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