Exercise · ZW-PROXIMAL-GRADIENT-METHOD-010 · ★★★ Advanced

Minimize \( \tfrac12(x-6)^2+|x| \) over \( \mathbb{R} \) with one proximal gradient step using \( t=1 \) from \( x_0=0 \). What is \( x_1 \)?

Choose one option.

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  • B. \( 5 \)

Explanation

The gradient at \( 0 \) is \( -6 \), so \( z=6 \). Applying soft thresholding with \( t\lambda=1 \) gives \( 5 \). Not applying the threshold gives \( 6 \).

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Review the concept: Proximal gradient method

Proximal gradient minimizes \( f(x)+g(x) \), with \( f \) convex and smooth and \( g \) convex but possibly nondifferentiable, alternating a gradient step on \( f \) and a proximal step on \( g \).

\[x_{k+1}=\operatorname{prox}_{t_k g}\left(x_k-t_k\nabla f(x_k)\right)\]

More practice problems: proximal gradient method

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