Gradient flow

The completed-square form gives global minima at $(1,-1)$ and $(-1,1)$, both with value $-1$, and a saddle point at $(0,0)$ with value zero. Negative levels split into two closed components; the zero contour joins their lobes at the saddle. The gradient flow $\dot{x}=-V_x,\dot{y}=-V_y$ decreases $V$ by the gradient-flow dissipation identity. A negative initial level traps each trajectory on its original side of $x=0$, because $V(0,y)\ge 0$.

A nonsmooth gradient flow driven by the subdifferential of a convex function. Explicit discretization gives the forward subgradient step; implicit discretization gives the proximal operator. Monotonicity of a convex subdifferential underlies uniqueness and contraction properties.

Positive mobility coefficients $m_r(x)$ give $dU/dt={\sum }_r{m}_r(x)({\partial }_{r}U{)}^2\ge 0$. With $m_r={\kappa }_r{x}_r$, the substitution $z_r=2\sqrt{x_r/{\kappa }_r}$ turns the equation into ordinary gradient ascent for the transformed potential on positive coordinates.

For the downward gradient flow $\dot{x}=-{\nabla }_{g}f$, the chain rule gives $df(x(t))/dt=-\Vert {\nabla }_{g}f(x(t)){\Vert }_g^2$. Integrating converts a bound on the decrease of $f$ into a bound on elapsed time wherever its Riemannian gradient is bounded away from zero.

If $f$ is bounded along a downward gradient flow, the total time spent where $\Vert {\nabla }_{g}f\Vert \ge \epsilon$ is at most $(\sup f-\inf f)/{\epsilon }^2$. The gradient-flow dissipation identity proves this by integrating the indicator of that region. It bounds all visits together, without requiring the visited region to be connected.

The formal $L^2$ gradient flow of this energy is $u_t=\mathrm{div}({\Phi }^{\prime }(r)\nabla u/r)$, with a Neumann boundary condition for no flux. The radial Hessian eigenvalues of the gradient integrand are ${\Phi }^{\prime \prime }(r)$ and ${\Phi }^{\prime }(r)/r$. Their signs distinguish convex forward diffusion from formal backward diffusion. The heat equation uses $\Phi (r)=r^2/2$, whereas total variation flow uses $\Phi (r)=r$ with a subgradient interpretation at zero.