Nonlinear Programs with Positively Bounded Jacobians

In [3], it is constructively demonstrated that if M is a square (not necessarily symmetric) matrix all of whose principal minors are positive, the quadratic program\[ (A1) \qquad {\text{minimize}}\quad z^T ( {Mz + q} )\quad {\text{subject to}}\quad Mz + q\geqq 0,\quad z\geqq 0, \] has an optimal solution satisfying the equation\[ ({\text{A2}})\qquad z^T ( {Mz + q} ) = 0. \]A different prooff is offered here. The analysis is then extended to programs of the form \[ ({\text{A3}})\qquad {\text{minimize}}\quad z^T W( z )\quad {\text{subject to}}\quad W( z )\geqq 0,\quad z\geqq 0, \] where W is a continuously differentiable mapping of real N-space into itself. The condition used to insure the existence of an optimal solution to (A3) is positive boundedness of the Jacobian matrix of the mapping W. Definition: A differentiable mapping $W:R^N \to R^N $ has a positively bounded Jacobian matrix, $J_w ( z )$, if there exists a real number $\delta $ such that $0 < \delta < 1$ and such that for every $z \in R^N $ each...

[1]  K Fan,et al.  Minimax Theorems. , 1953, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Philip Wolfe,et al.  An algorithm for quadratic programming , 1956 .

[3]  M. Sion On general minimax theorems , 1958 .

[4]  W. S. Dorn,et al.  Self-Dual Quadratic Programs , 1961 .

[5]  R. Cottle Note on a Fundamental Theorem in Quadratic Programming , 1964 .

[6]  D. Gale,et al.  The Jacobian matrix and global univalence of mappings , 1965 .

[7]  C. E. Lemke,et al.  Bimatrix Equilibrium Points and Mathematical Programming , 1965 .