Complementarity problem
From Wikimization
(→Any solution of a nonlinear optimization problem on a closed convex cone is a solution of a nonlinear complementarity problem) |
(→A convex optimization problem on a closed convex cone is equivalent to a nonlinear complementarity problem) |
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<math>(\mathbb H,\langle\cdot,\cdot\rangle)</math> and <math>f:\mathbb H\to\mathbb R</math> | <math>(\mathbb H,\langle\cdot,\cdot\rangle)</math> and <math>f:\mathbb H\to\mathbb R</math> | ||
a differentiable convex function. Then the [[Complementarity_problem#Nonlinear_optimization_problems | nonlinear optimization problem]] <math>NOPT(f,\mathcal K)</math> is equivalent to the [[Complementarity_problem#Nonlinear_complementarity_problems | nonlinear complementarity problem]] | a differentiable convex function. Then the [[Complementarity_problem#Nonlinear_optimization_problems | nonlinear optimization problem]] <math>NOPT(f,\mathcal K)</math> is equivalent to the [[Complementarity_problem#Nonlinear_complementarity_problems | nonlinear complementarity problem]] | ||
- | <math>NCP( | + | <math>NCP(\nabla f,\mathcal K).</math> |
=== Proof === | === Proof === | ||
- | <math>NOPT(f,\mathcal K)</math> [[Complementarity_problem#Every_variational_inequality_is_equivalent_to_a_fixed_point_problem | is equivalent to]] <math>VI( | + | <math>NOPT(f,\mathcal K)</math> [[Complementarity_problem#Every_variational_inequality_is_equivalent_to_a_fixed_point_problem | is equivalent to]] <math>VI(\nabla f,\mathcal K)</math> which [[Complementarity_problem#Every_variational_inequality_defined_on_a_closed_convex_cone_is_equivalent_to_a_complementarity_problem| is equivalent to]] <math>NCP(\nabla f,\mathcal K).</math> |
== Fat nonlinear programming problem == | == Fat nonlinear programming problem == |
Revision as of 22:29, 17 August 2009
Sándor Zoltán Németh
Fixed point problems
Let be a set and a mapping. The fixed point problem defined by is the problem
Nonlinear complementarity problems
Let be a closed convex cone in the Hilbert space and a mapping. Recall that the dual cone of is the closed convex cone where is the polar of The nonlinear complementarity problem defined by and is the problem
Every nonlinear complementarity problem is equivalent to a fixed point problem
Let be a closed convex cone in the Hilbert space and a mapping. Then the nonlinear complementarity problem is equivalent to the fixed point problem where is the identity mapping defined by and is the projection onto
Proof
For all denote and Then
Suppose that is a solution of Then with and Hence, by using Moreau's theorem, we get Therefore, is a solution of
Conversely, suppose that is a solution of Then and via Moreau's theorem
Hence, . Thus, . Moreau's theorem also implies that In conclusion, and Therefore, is a solution of
An alternative proof without Moreau's theorem
Variational inequalities
Let be a closed convex set in the Hilbert space and a mapping. The variational inequality defined by and is the problem
Every variational inequality is equivalent to a fixed point problem
Let be a closed convex set in the Hilbert space and a mapping. Then the variational inequality is equivalent to the fixed point problem
Proof
is a solution of if and only if By using the characterization of the projection the latter equation is equivalent to
for all But this holds if and only if is a solution of
Remark
The next section shows that the equivalence of variational inequalities and fixed point problems is much stronger than the equivalence of nonlinear complementarity problems and fixed point problems because each nonlinear complementarity problem is a variational inequality defined on a closed convex cone.
Every variational inequality defined on a closed convex cone is equivalent to a complementarity problem
Let be a closed convex cone in the Hilbert space and a mapping. Then the nonlinear complementarity problem is equivalent to the variational inequality
Proof
Suppose that is a solution of Then and Hence,
for all Therefore, is a solution of
Conversely, suppose that is a solution of Then and
for all Particularly, taking and , respectively, we get Thus, for all or equivalently In conclusion, and Therefore, is a solution of
Concluding the alternative proof
Since is a closed convex cone, the nonlinear complementarity problem is equivalent to the variational inequality which is equivalent to the fixed point problem
Implicit complementarity problems
Let be a closed convex cone in the Hilbert space and two mappings. Recall that the dual cone of is the closed convex cone where is the polar of The implicit complementarity problem defined by and the ordered pair of mappings is the problem
Every implicit complementarity problem is equivalent to a fixed point problem
Let be a closed convex cone in the Hilbert space and two mappings. Then the implicit complementarity problem is equivalent to the fixed point problem where is the identity mapping defined by
Proof
For all denote and Then
Suppose that is a solution of Then with and Hence, by using
Moreau's theorem,
we get Therefore, is a solution of
Conversely, suppose that is a solution of Then and via Moreau's theorem
Hence, . Thus, . Moreau's theorem also implies that In conclusion, and Therefore, is a solution of
Remark
If in particular, we obtain the result every nonlinear complementarity problem is equivalent to a fixed point problem. But the more general result, every implicit complementarity problem is equivalent to a fixed point problem, has no known connection with variational inequalities. Using Moreau's theorem is therefore essential for proving the latter result.
Nonlinear optimization problems
Let be a closed convex set in the Hilbert space and a function. The nonlinear optimization problem defined by and is the problem
Any solution of a nonlinear optimization problem is a solution of a variational inequality
Let be a closed convex set in the Hilbert space and a differentiable function. Then any solution of the nonlinear optimization problem is a solution of the variational inequality where is the gradient of
Proof
Let be a solution of and an arbitrary point. Then by convexity of we have Hence, and therefore
Therefore, is a solution of
A convex optimization problem is equivalent to a variational inequality
Let be a closed convex set in the Hilbert space and a differentiable convex function. Then the nonlinear optimization problem is equivalent to the variational inequality where is the gradient of
Proof
Any solution of is a solution of
Conversely, suppose that is a solution of Hence, by using the convexity of we have for all Therefore, is a solution of
Any solution of a nonlinear optimization problem on a closed convex cone is a solution of a nonlinear complementarity problem
Let be a closed convex cone in the Hilbert space and a differentiable function. Then any solution of the nonlinear optimization problem is a solution of the nonlinear complementarity problem
Proof
Any solution of is a solution of which is equivalent to
A convex optimization problem on a closed convex cone is equivalent to a nonlinear complementarity problem
Let be a closed convex cone in the Hilbert space and a differentiable convex function. Then the nonlinear optimization problem is equivalent to the nonlinear complementarity problem
Proof
is equivalent to which is equivalent to
Fat nonlinear programming problem
Let be a function, and a fat matrix of full rank Then the problem
is called fat nonlinear programming problem.
Any solution of a fat nonlinear programming problem is a solution of a nonlinear complementarity problem defined by a polyhedral cone
Let be a differentiable function, and a matrix of full rank If is a solution of the fat nonlinear programming problem then is a solution of the nonlinear complementarity problem where is a particular solution of the linear system of equations is the polyhedral cone defined by
and is defined by
Proof
Let be a solution of Then it is easy to see that is a solution of where is defined by It follows that is a solution of because
Remark
If is convex, then the converse of the above results also holds.
We note that there are also many nonlinear programming problems defined by skinny matrices (i.e., m>n) that can be reduced to complementarity problems.
Since a very large class of nonlinear programming problems can be reduced to nonlinear complementarity problems, the importance of nonlinear complementarity problems on polyhedral cones is obvious both from theoretical and practical point of view.