Papers › Nonsmooth Convex Optimization using the Specular Gradient Method with Root-Linear Convergence
Nonsmooth Convex Optimization using the Specular Gradient Method with Root-Linear Convergence
Kiyuob Jung, Jehan Oh
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We propose the specular gradient method for one-dimensional convex optimization. Assuming that the minimum is attained and a suitable initial distance bound holds, we establish R-linear convergence using normalized steps of geometrically decreasing length. Neither strong convexity nor differentiability is required.
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