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For a convex objective with an $L$-Lipschitz gradient, Nesterov's accelerated gradient method reaches objective error $\delta$ in $O(\sqrt{L{R}^2/\delta })$ iterations when a minimizer lies within distance $R$ of the initial point.

For the Euclidean norm, rotational symmetry and minimizing the radial objective give the radial soft thresholding formulaIndeed, if $r=\Vert x{\Vert }_2$, an optimal $u$ is a nonnegative scalar multiple of $x$; minimizing $s+(r-s{)}^2/(2\tau )$ over $s\ge 0$ gives $s=(r-\tau {)}_{+}$. The gradient of a Moreau envelope is consequentlyAt $x=0$ this is $\{0\}$, and the two expressions agree when $\Vert x{\Vert }_2=\tau$. The Moreau envelope of the Euclidean norm itself isThus a quadratic core replaces the nondifferentiable tip while the outer gradient remains the normalized radial direction. The Euclidean norm has an unbounded effective domain, unlike the earlier bounded-domain hypothesis; the preceding argument explicitly shows that this restriction is unnecessary here.

For convex optimization, this Moreau–Yosida regularisation permits gradient descent and other methods for functions with a Lipschitz gradient, using a gradient Lipschitz constant $1/\tau$. It approximates the norm uniformly: $0\le \Vert x{\Vert }_2-f_{\tau }(x)\le \tau /2$. Smaller $\tau$ improves approximation but makes the permitted gradient steps smaller. Minimizing $f_{\tau }$ alone preserves the minimizers and minimum value of $f$; replacing one term in a larger objective can shift the minimizer, so the smoothing parameter controls that approximation error.

To first order in $n_{ϵ}$, replace $q$ by $2n_{ϵ}$ and use the known incident field in the outgoing integral. The Born approximation for scalar wave scattering givesBoth the $n_{ϵ}^2$ contrast term and the internal-field correction are second order for fixed geometry in the perturbative regime established in part i.

Specify the data space before taking an adjoint operator. For example, let $X=L^2(B)$ and $Y=L^2(\Gamma )$, where $\Gamma$ is a bounded measurement surface separated from $B$. Define the linear operatorFor bounded incident field this is a Hilbert-Schmidt operator, hence a compact operator. The equation is $A{n}_{ϵ}=y$, with $y={\psi }_s{|}_{\Gamma }$. Other sampling geometries give corresponding data spaces and weights; the paper does not specify one. Using the usual complex $L^2$ inner products, its adjoint operator isThe complex conjugations are required by the adjoint operator identity, not by wave reciprocity alone.

The Landweber iteration starts from $f_0=0$ and applies gradient descent to $\frac{1}{2}\Vert Af-y{\Vert }^2$:Each step back-propagates the data residual. The Landweber relaxation parameter controls stability, and early stopping of Landweber iteration prevents small singular values from amplifying measurement errors. For an explicitly real-valued index contrast, use the real Hilbert space structure and replace $A^{\ast }$ by $\mathrm{Re}{A}^{\ast }$ in this update. Additional sign or support constraints require corresponding projections; none are assumed here.

Minimize the data-misfit functional $J(x)=\frac{1}{2}\Vert Ax-y{\Vert }^2$. For a perturbation $h$, $DJ(x)[h]=\mathrm{Re}⟨A^{\ast }(Ax-y),h⟩$, so its gradient is $A^{\ast }(Ax-y)$. Gradient descent gives Landweber iteration:On a singular component, the iteration error is multiplied by $1-\tau {\sigma }_j^2$, whose modulus is less than one. Starting from zero preserves orthogonality to $\ker A$ and makes the exact-data iterates converge to $x^{†}$.

For the unit-step formulas and noise bound used below, rescale so that $\tau =1$ and $\Vert A\Vert \le 1$. Finite iteration count bounds amplification of small singular-value components; the stopping count provides the regularization parameter.

Starting from $x_0\in {\mathbb{R}}^n$, gradient descent repeatedly computes the gradient and updatesstopping when the gradient norm, step, or objective decrease is sufficiently small.

The Hessian bounds say that $f$ is $\alpha$-strongly convex and has $\beta$-smooth gradient. With $\eta =1/\beta$,Thus the iteration count isconvergence becomes slower linearly with the condition number $\kappa =\beta /\alpha$.

Forthe Hessian matrix is $\mathrm{diag}(2,200,20000)$, soTakeThenwhose Hessian is $2I$ and whose condition number is $1$.

The maximizer defining $g^{\ast }(x)$ is ${\mathrm{prox}}_{tf}(x)$, soConsequentlyThus the proximal point algorithm for $f$ is ordinary gradient descent with step $t$ on its smooth Moreau envelope.

The gradient descent iteration isFor the error $e^{(k)}=x^{(k)}-A^{-1}b$,Part (a) shows that convergence for every initial vector is equivalent to
$|1-\alpha {\lambda }_i|<1$ for every eigenvalue of $A$. Since all eigenvalues are positive,

Forthe Frechet derivative in direction $h$ isThus the Hilbert-space gradient isThe gradient descent update with step size $\gamma$ is consequentlywhich is exactly Landweber iteration. Its stationary points solve the normal equation and minimize the convex quadratic functional $J$.

Gradient descent is $x_{t+1}=x_t-\eta \nabla f(x_t)$. Integrating the Hessian along a segment gives the descent lemma. Applying it to the hinted $z$ and convexity yields cocoercivity: $(\nabla f(x)-\nabla f(y){)}^T(x-y)\ge \Vert \nabla f(x)-\nabla f(y){\Vert }^2/\beta$. Apply this to $\varphi =f-\alpha \Vert x{\Vert }^2/2$, whose Hessian lies between $0$ and $(\beta -\alpha )I$, and rearrange to obtain the displayed strengthened inequality. With $y=x^{\ast }$, $\nabla f(x^{\ast })=0$, and $\eta =2/(\alpha +\beta )$, expansion of the squared update gives contraction factor $((\beta -\alpha )/(\beta +\alpha ){)}^2$ per step and the stated bound.