Frank–Wolfe Algorithm

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In mathematical optimization, the **reduced gradient method** of Frank and Wolfe is an iterative method for nonlinear programming. Also known as the **Frank–Wolfe algorithm** and the *convex combination algorithm*, the reduced gradient method was proposed by Marguerite Frank and Phil Wolfe in 1956 as an algorithm for quadratic programming. In phase one, the reduced gradient method finds a feasible solution to the linear constraints, if one exists. Thereafter, at each iteration, the method takes a descent step in the negative gradient direction, so reducing the objective function; this gradient descent step is "reduced" to remain in the polyhedral feasible region of the linear constraints. Because quadratic programming is a generalization of linear programming, the reduced gradient method is a generalization of Dantzig's simplex algorithm for linear programming.

The reduced gradient method is an iterative method for nonlinear programming, a method that need not be restricted to quadratic programming. While the method is slower than competing methods and has been abandoned as a general purpose method of nonlinear programming, it remains widely used for specially structured problems of large scale optimization. In particular, the reduced gradient method remains popular and effective for finding approximate minimum–cost flow in transportation networks, which often have enormous size.

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The reduced gradient method is an iterative method for nonlinear programming, a method that need not be restricted to quadratic programming. While the method is slower than competing methods and has been abandoned as a general purpose method of nonlinear programming, it remains widely used for specially structured problems of large scale optimization. In particular, the reduced gradient method remains popular and effective for finding approximate minimum–cost flow in transportation networks, which often have enormous size.

- Minimize <math> f(mathbf) =...... ...

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