Zobrazeno 1 - 10
of 1 589
pro vyhledávání: '"J B, Rosen"'
Autor:
Al-Khayyal, Faiz
Publikováno v:
SIAM Review, 1990 Jun 01. 32(2), 310-312.
Externí odkaz:
https://www.jstor.org/stable/2030536
Autor:
Faiz Al-Khayyal
Publikováno v:
SIAM Review. 32:310-312
Publikováno v:
Mathematics of Computation, 1972 Oct 01. 26(120), 1020-1021.
Externí odkaz:
https://www.jstor.org/stable/2005896
Publikováno v:
SIAM Review; June 1990, Vol. 32 Issue: 2 p310-312, 3p
Autor:
Whitehouse, Erin R.1,2,3, Gerloff, Nancy1, English, Randall1, Reckling, Stacie K.4, Alazawi, Mohammed A.5, Fuschino, Meghan6, St George, Kirsten6, Lang, Daniel5, Rosenberg, Eli S.5, Omoregie, Enoma7, Rosen, Jennifer B.7, Kitter, Alyse8, Korban, Colin8, Pacilli, Massimo8, Jeon, Trisha9, Coyle, Joseph10, Faust, Russell A.11, Xagoraraki, Irene12, Miyani, Brijen12, Williams, Charles13
Publikováno v:
Emerging Infectious Diseases. Nov2024, Vol. 30 Issue 11, p2279-2287. 9p.
Nonlinear Programming contains the proceedings of a Symposium on Nonlinear Programming held in Madison, Wisconsin on May 4-6, 1970. This book emphasizes algorithms and related theories that lead to efficient computational methods for solving nonlinea
Autor:
John Glick, J. B. Rosen
Publikováno v:
Journal of Global Optimization. 36:461-469
The problem of approximating m data points (x i , y i ) in $$\mathfrak{R}^{n+1}$$ , with a quadratic function q(x, p) with s parameters, m ? s, is considered. The parameter vector $$p\in \mathfrak{R}^s$$ is to be determined so as to satisfy three con
Publikováno v:
Journal of Global Optimization. 34:475-488
Motivated by the fact that important real-life problems, such as the protein docking problem, can be accurately modeled by minimizing a nonconvex piecewise-quadratic function, a nonconvex underestimator is constructed as the minimum of a finite numbe
Publikováno v:
Computational Optimization and Applications. 34:35-45
A function on Rn with multiple local minima is approximated from below, via linear programming, by a linear combination of convex kernel functions using sample points from the given function. The resulting convex kernel underestimator is then minimiz