A reduced subgradient algorithm
A reduced subgradient algorithm
The authors describe an iterative method for the approximate solution of nondifferentiable convex programming problems. The problems incorporate linear equality and inequality constraints \((Ax=b\), \(x\geq 0)\); the method combines Wolfe's well-known reduced gradient algorithm with the bundle method of nonsmooth optimization. The algorithm is explained clearly, convergence is proved, and five numerical examples are presented.
- University of Namur Belgium
Methods of successive quadratic programming type, Convex programming, linearly constrained minimization, nondifferentiable convex programming, Numerical mathematical programming methods, reduced gradient algorithm, bundle method, approximate solution, nonsmooth optimization
Methods of successive quadratic programming type, Convex programming, linearly constrained minimization, nondifferentiable convex programming, Numerical mathematical programming methods, reduced gradient algorithm, bundle method, approximate solution, nonsmooth optimization
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