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  Browse All Reviews > Mathematics Of Computing (G) > Numerical Analysis (G.1) > Optimization (G.1.6) > Quadratic Programming Methods (G.1.6...)  
 
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  1-5 of 5 Reviews about "Quadratic Programming Methods (G.1.6...)": Date Reviewed
  Survey on combinatorial register allocation and instruction scheduling
Lozano R., Schulte C. ACM Computing Surveys 52(3): 1-50, 2019.  Type: Article

Compilers use heuristic algorithms to quickly produce inexact solutions for register assignment (assigning values to central processing unit [CPU] registers) and instruction scheduling (ordering instructions for execution)....

Jan 26 2021
  Shape optimization with computational fluid dynamics
El-Sayed M., Sun T., Berry J. Advances in Engineering Software 36(9): 607-613, 2005.  Type: Article

Determining optimal parameters for the design of curvy ducts poses computational challenges. Visual exploration of the transformations of curvilinear duct design space and response parameters requires optimization tools beyond the inev...

Jan 26 2006
  Minimizing quadratic functions subject to bound constraints with the rate of convergence and finite termination
Dostál Z., Schöberl J. Computational Optimization and Applications 30(1): 23-43, 2005.  Type: Article

The application of finite element methods to the solution of many variational inequalities leads to the minimization of a quadratic function of a large number of variables with bound constraints. Unconstrained problems of this size are...

Aug 2 2005
  Perturbation to enhance support vector machines for classification
To K., Lim C. Journal of Computational and Applied Mathematics 163(1): 233-239, 2004.  Type: Article

Support vector machines (SVM) are learning algorithms, frequently used to solve classification and regression tasks, and supporting the linear separation of a binary class of data. SVM techniques have been extensively used to solve a l...

May 19 2004
  A method of trust region type for minimizing noisy functions
Elster C., Neumaier A. Computing 58(1): 31-46, 1997.  Type: Article

An algorithm to optimize a noisy function in a few variables is given. A function is considered noisy if its evaluation is imprecise due to stochastic measurement errors. The authors were working in the domain of chemical experiments, ...

Jun 1 1998
 
 
 
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