Roger Fletcher mainly investigates Mathematical optimization, Nonlinear programming, Algorithm, Quadratic programming and Range. His work in the fields of Minification overlaps with other areas such as Descent. His Nonlinear programming research is multidisciplinary, incorporating perspectives in Optimization problem, Integer programming and Sequential quadratic programming.
His Algorithm research incorporates themes from Broyden–Fletcher–Goldfarb–Shanno algorithm and Metric. His Quadratically constrained quadratic program study in the realm of Quadratic programming connects with subjects such as Random optimization and Derivative-free optimization. His Range research is multidisciplinary, incorporating elements of Quadratic equation, Approximation algorithm, Norm, Filter and Function.
His primary scientific interests are in Mathematical optimization, Nonlinear programming, Algorithm, Hessian matrix and Sequential quadratic programming. His work carried out in the field of Mathematical optimization brings together such families of science as Range, Line search and Trust region. His Nonlinear programming research focuses on subjects like Penalty method, which are linked to Function.
Roger Fletcher works mostly in the field of Algorithm, limiting it down to topics relating to Numerical analysis and, in certain cases, Quadratic equation and Gradient method. Roger Fletcher has included themes like Local convergence and Filter in his Sequential quadratic programming study. Particularly relevant to Quadratically constrained quadratic program is his body of work in Quadratic programming.
His primary areas of investigation include Mathematical optimization, Mathematical analysis, Constrained optimization, Nonlinear programming and Algorithm. His Mathematical optimization research includes themes of Dimension, Numerical analysis and Hessian matrix. His work in the fields of Mathematical analysis, such as Stationary point and Maxima and minima, overlaps with other areas such as Nonlinear conjugate gradient method and Stability.
The Constrained optimization study combines topics in areas such as Quadratic programming, Active set method and Algebra. His Nonlinear programming research includes elements of Trust region, Linear-fractional programming, Integer programming and Artificial intelligence. His Algorithm research incorporates themes from Lanczos resampling, Quadratic equation, Method of steepest descent and Minification.
Roger Fletcher mainly focuses on Mathematical optimization, Nonlinear programming, Algorithm, Feature and Compact convergence. His study in the field of Minification also crosses realms of Constraint logic programming. His study in Nonlinear programming is interdisciplinary in nature, drawing from both Linear-fractional programming, CUTEr, Trust region and Artificial intelligence.
The concepts of his Algorithm study are interwoven with issues in Dimension, Numerical analysis, Lanczos resampling and Method of steepest descent. Roger Fletcher has included themes like Local convergence and Sequential quadratic programming in his Feature study.
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Practical Methods of Optimization
A Rapidly Convergent Descent Method for Minimization
R. Fletcher;M. J. D. Powell.
The Computer Journal (1963)
Function minimization by conjugate gradients
R. Fletcher;C. M. Reeves.
The Computer Journal (1964)
Practical Methods of Optimization.
Christoph Witzgall;R. Fletcher.
Mathematics of Computation (1989)
A new approach to variable metric algorithms
The Computer Journal (1970)
Practical methods of optimization; (2nd ed.)
Nonlinear programming without a penalty function
Roger Fletcher;Sven Leyffer.
Mathematical Programming (2002)
Conjugate gradient methods for indefinite systems
Lecture Notes in Mathematics (1976)
Solving mixed integer nonlinear programs by outer approximation
Roger Fletcher;Sven Leyffer.
Mathematical Programming (1994)
On the Global Convergence of a Filter--SQP Algorithm
Roger Fletcher;Sven Leyffer;Philippe L. Toint.
Siam Journal on Optimization (2002)
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