MATH 451ANonlinear Optimization - A
- Faculty
- Mathematics
- Last listed
- 1975–76
No longer offered. It’s in Waterloo’s scanned calendars from 1972–73 to 1975–76; the 1995–96 to 2023–24 calendars are on ucalendar.
Recent algorithms for constrained and unconstrained differentiable optimization problems will be presented. Topics will include first and second order Newton methods, quasi-Newton methods, conjugate gradient directions, feasible direction methods, and penalty algorithms. Applications and implementations will be given. : ‘ Recommended for students interested in operations research, industrial engineering, computer science and for those wishing to pursue graduate courses in mathematical programming. 2 hours lectures, one term
As printed in the 1975–76 calendar, read by OCR; the scanned page is the original.
Check it elsewhere
In the scanned calendars
How it read in each calendar from 1972–73 to 1975–76, newest first. The text is OCR of the scanned pages; years whose wording matches are shown once, from the clearest scan. Each year links to its page on uwloo and to that page of the scanned PDF.
1975–76 1 calendar
Recent algorithms for constrained and unconstrained
differentiable optimization problems will be presented.
Topics will include first and second order Newton
methods, quasi-Newton methods, conjugate gradient
directions, feasible direction methods, and penalty
algorithms. Applications and implementations will
be given. : ‘ Recommended for students interested in operations
research, industrial engineering, computer science
and for those wishing to pursue graduate courses in
mathematical programming.
2 hours lectures, one term
Not in the 1973–74 to 1974–75 calendars.
1972–73 1 calendar
A study of recent algorithms for minimizing nonlinear differentiable functions subject to nonlinear constraints. Extentions of classical calculus methods to semidefinite quadratic forms. Unconstrained univariate optimization with examples of the four basic approaches. Unconstrained multivariate optimization including first and second order Newton methods, quasi-Newton methods, and conjugate gradient directions. Constrained optimization including first and second order Newton methods, quasi-Newton methods, and conjugate gradient directions. Constrained optimization including nonlinear transformations, feasible direction methods, projection algorithms and penalty functions. Recommended for graduate level nonlinear programming. 2 hours lectures, one term. Prerequisites: Differential Calculus (237); Elementary Fortran.