Results 1 - 10 of 46,208

Constrained model predictive control: Stability and optimality

by D. Q. Mayne, J. B. Rawlings, C. V. Rao, P. O. M. Scokaert - AUTOMATICA , 2000
"... Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon open-loop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence and t ..."
Abstract - Cited by 738 (16 self) - Add to MetaCart
Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon open-loop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence

SNOPT: An SQP Algorithm For Large-Scale Constrained Optimization

by Philip E. Gill, Walter Murray, Michael A. Saunders , 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
Abstract - Cited by 597 (24 self) - Add to MetaCart
Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first

A Limited Memory Algorithm for Bound Constrained Optimization

by Richard H. Byrd, Peihuang Lu, Jorge Nocedal, Ciyou Zhu - SIAM JOURNAL ON SCIENTIFIC COMPUTING , 1994
"... An algorithm for solving large nonlinear optimization problems with simple bounds is described. It is based ..."
Abstract - Cited by 572 (9 self) - Add to MetaCart
An algorithm for solving large nonlinear optimization problems with simple bounds is described. It is based

Nonlinear total variation based noise removal algorithms

by Leonid I. Rudin, Stanley Osher, Emad Fatemi , 1992
"... A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using the g ..."
Abstract - Cited by 2271 (51 self) - Add to MetaCart
A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using

Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks

by Ros Tassiulas, Anthony Ephremides - IEEE Transactions on Automatic Control , 1992
"... Abstruct-The stability of a queueing network with interdependent servers is considered. The dependency of servers is described by the definition of their subsets that can be activated simultaneously. Multihop packet radio networks (PRN’s) provide a motivation for the consideration of this system. We ..."
Abstract - Cited by 949 (19 self) - Add to MetaCart
is stable. A policy m,, is obtained which is optimal in the sense that its stability region Cn0 is a superset of the stability region of every other scheduling policy. The stability region Cmo is characterized. Finally, we study the behavior of the network for arrival rates that lie outside the stability

No Free Lunch Theorems for Optimization

by David H. Wolpert, et al. , 1997
"... A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of “no free lunch ” (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performan ..."
Abstract - Cited by 961 (10 self) - Add to MetaCart
issues addressed include time-varying optimization problems and a priori “head-to-head” minimax distinctions between optimization algorithms, distinctions that result despite the NFL theorems’ enforcing of a type of uniformity over all algorithms.

The program dependence graph and its use in optimization

by Jeanne Ferrante, Karl J. Ottenstein, Joe D. Warren - ACM Transactions on Programming Languages and Systems , 1987
"... In this paper we present an intermediate program representation, called the program dependence graph (PDG), that makes explicit both the data and control dependence5 for each operation in a program. Data dependences have been used to represent only the relevant data flow relationships of a program. ..."
Abstract - Cited by 996 (3 self) - Add to MetaCart
. Control dependence5 are introduced to analogously represent only the essential control flow relationships of a program. Control dependences are derived from the usual control flow graph. Many traditional optimizations operate more efficiently on the PDG. Since dependences in the PDG connect

Systematic design of program analysis frameworks

by Patrick Cousot, Radhia Cousot - In 6th POPL , 1979
"... Semantic analysis of programs is essential in optimizing compilers and program verification systems. It encompasses data flow analysis, data type determination, generation of approximate invariant ..."
Abstract - Cited by 765 (50 self) - Add to MetaCart
Semantic analysis of programs is essential in optimizing compilers and program verification systems. It encompasses data flow analysis, data type determination, generation of approximate invariant

Training Support Vector Machines: an Application to Face Detection

by Edgar Osuna, Robert Freund, Federico Girosi , 1997
"... We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision sur ..."
Abstract - Cited by 727 (1 self) - Add to MetaCart
surfaces are found by solving a linearly constrained quadratic programming problem. This optimization problem is challenging because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points. We present a decomposition algorithm that guarantees

Topology Control of Multihop Wireless Networks using Transmit Power Adjustment

by Ram Ramanathan , Regina Rosales-hain , 2000
"... We consider the problem of adjusting the transmit powers of nodes in a multihop wireless network (also called an ad hoc network) to create a desired topology. We formulate it as a constrained optimization problem with two constraints- connectivity and biconnectivity, and one optimization objective- ..."
Abstract - Cited by 688 (3 self) - Add to MetaCart
We consider the problem of adjusting the transmit powers of nodes in a multihop wireless network (also called an ad hoc network) to create a desired topology. We formulate it as a constrained optimization problem with two constraints- connectivity and biconnectivity, and one optimization objective
Results 1 - 10 of 46,208