Define the softmax weights
They obey and . The gradient is their weighted mean,
Differentiating once more gives the covariance-form Hessian matrix
For every unit vector ,
where . The Hessian is a covariance matrix, so it is positive semidefinite; the displayed upper bound also gives in the Loewner order. Consequently has a Lipschitz gradient with
The first-order subgradient inequality for a differentiable convex function gives
and, after interchanging and ,
Adding and rearranging yields
Thus the gradient is a monotone operator.
Using the harmless normalization , the squared-loss empirical risk for linear prediction is
with explicit gradient
Starting from any , projected gradient descent with step sizes is
For a fully explicit update, write and . The projection from part (e) is
Every iterate therefore lies in the prescribed hypothesis class, and any convergent run under the standard convex-optimization step-size conditions targets its empirical-risk minimizer.
The Stokes theorem states that for an oriented smooth surface with positively oriented boundary ,
A field is conservative vector field if its line integral is path-independent, equivalently if every closed line integral vanishes. If , the fundamental theorem for line integrals gives
This is zero for every closed curve, so every globally defined gradient field is conservative.
The first field is
so its integral around is zero and it is conservative with potential .
Along , and
so
Thus is not conservative and cannot be a gradient on the stated punctured region.
Finally,
Its integral around is zero, and it is conservative with the displayed potential. Therefore the three integrals are respectively
and exactly are conservative gradient fields.
Vanishing net charge density does not require vanishing current. Positive and negative charge carriers can cancel in charge density while their oppositely directed motions add to a nonzero current. Charge conservation only requires
for magnetostatics this becomes .
Because , one may introduce a magnetic vector potential with
It is not unique: gives the same field for any scalar .
For the stated current,
so it is consistent with stationary charge conservation. Direct calculation gives
Thus is a Beltrami field. For , choose
Then and . A convenient Coulomb-gauge potential is
since and . Gradient gauge terms may of course be added.
If , the current is the constant field . The formulas involving do not apply; one valid choice is
Newton's method for minimization uses
For a quantitative local bound, suppose on a convex neighbourhood containing the iterates that
with , and let be the minimizer. The integral form of the gradient and the Hessian Lipschitz bound give the quadratic convergence bound for Newton's method
If , induction yields
Since ,
For
the Frechet derivative in direction is
Thus the Hilbert-space gradient is
The gradient descent update with step size is consequently
which is exactly Landweber iteration. Its stationary points solve the normal equation and minimize the convex quadratic functional .
Take downward velocity as positive. Lubrication theory reduces the axial momentum equation to a constant pressure gradient, and the no-slip conditions are , . Twice integrating gives
The flux per unit circumference and the total gap flux are
The shear at the cylinder is
with the fluid traction on the falling cylinder opposing its motion.
The descending solid displaces volume at rate , so closed-container mass conservation gives
Since , the Couette-flux term is smaller, and
The pressure or form drag is
Using the pressure-dominated part of , the side shear force is of order
so . Balancing form drag against the excess weight gives
This is annular lubrication drag on a settling cylinder.
The diffusive flux is . The total outward flux through both ends is therefore
It diverges as : the initially uniform profile is incompatible with the suddenly imposed zero boundary values, creating an idealized infinite initial gradient.
With streamwise and normal velocities , zero exterior pressure gradient gives the steady boundary-layer equations
At the organism,
and matching to the quiescent exterior requires as .
The gradient is . Completing the square gives
for , so is the unique global minimizer. Since ,
For ,
The objective has gradient and Hessian , which is negative definite. Its unique maximizer is therefore
Pressure acts normally to the circular cylinder and so has no moment about its axis. To leading lubrication order, the tangential shear at the cylinder is
Substitution of the pressure gradient gives
A torque-free cylinder requires . Also,
Using , the torque condition reduces to
Hence
Every term in part (d) is a spatial gradient. Extracting the longitudinal scalar coefficient, up to a spatially homogeneous function that can be absorbed into the zero mode, yields the Cosmological Euler equation in synchronous gauge:
Yes. Synchronous gauge in cosmology uses freely falling time lines and sets the lapse and shift perturbations to zero, so no gravitational-potential force appears explicitly in this component of momentum conservation. Metric perturbations still affect the other field equations, the evolution of matter variables, and the relation between coordinate-dependent variables and gauge-invariant observables; synchronous gauge also retains residual gauge modes.
In Newtonian gauge in cosmology, the time-time metric perturbation is a Newtonian gravitational potential. Its spatial gradient therefore appears explicitly as a force term in the Euler equation. The difference is a coordinate representation of the same covariant conservation law.
Boundary-layer theory assumes steady incompressible high-Reynolds-number flow, , weak streamwise viscous diffusion, nearly constant pressure across the layer, and outer pressure gradient .
For ,
At the normal velocity vanishes; along the upper wall . Boundary-layer scaling gives
Set . The Falkner-Skan equation becomes
so , , , with
and . Finally
requires ; thus and the tip region violates the approximation.
For smooth compactly supported , multiply by . The removed term has norm , while its gradient terms are in dimension three. Thus it tends to zero in . Density then proves .
Gradient boosting initializes a constant score and iterates: compute logistic negative gradients (for loss ), fit the base regressor to these pseudo-responses, choose a line-search step, and set . For a stump , sort each coordinate once. Sweeping the split positions while maintaining left and right sums gives the optimal and squared error in per coordinate, hence overall.