A magneto-optical trap combines laser cooling with a position-dependent magnetic field and suitable beam electromagnetic polarization to produce a restoring force. Its spatial confinement is additional to the velocity damping of uniform optical molasses.

Real atoms have multiple internal quantum states. Optical pumping and spatially varying electromagnetic polarization can remove kinetic energy by additional mechanisms, producing sub-Doppler cooling below the ideal Doppler cooling limit. In polarization-gradient cooling, state-dependent energy shifts from the Stark effect and delayed optical pumping make atoms repeatedly climb potential energy hills. This mechanism needs the internal-state structure omitted by the independent-beam two-level system above. ^[ref].

William D. Phillips's team tested unexpectedly cold sodium molasses with time-of-flight atom detection. A probe above the cloud initially saw no atoms; deliberately heating them verified that the detector worked. Moving the probe below the cloud produced arrival signals consistent with temperatures below the Doppler cooling limit. Independent release/recapture checks used sharply apertured beams and included gravity; uncertain cloud and probe geometry still limited the inferred temperature. These controls tested the thermometer as well as the cooling theory. ^[ref].

Optical molasses surrounds slow chemical atoms with opposing red-detuned laser beams, usually three pairs along perpendicular axes. In the elementary Doppler cooling picture, each pair damps one component of velocity. The result resembles viscous drag, even though the atoms are moving through a vacuum rather than a liquid.

Uniform balanced optical molasses supplies velocity damping, without a restoring force towards a chosen position. An atom at rest has zero mean force wherever it sits within the uniform illuminated region. Without noise or external forces, $m\dot{v}=-\alpha v$ givesThe atom approaches a position determined by its initial condition, not the beam intersection. Recoil-driven diffusion and gravity eventually remove atoms from finite beams. A long residence time in optical molasses is therefore compatible with the absence of position trapping. An optical trap provides spatial confinement by a different force mechanism; a magneto-optical trap adds a position-dependent magnetic field and suitable beam electromagnetic polarization to create a restoring force.

Consider equal opposing laser beams along the $x$ axis and an atom with velocity $v$. Let ${\omega }_L$ be their angular frequency, ${\omega }_0$ the atomic resonance angular frequency, $k=2\pi /\lambda >0$ their wavenumber, and $\delta ={\omega }_L-{\omega }_0$ the laser detuning. Red detuning means $\delta <0$. To first order in $v/c$, the atom seesFor small positive $v$, the beam travelling towards negative $x$ is closer to resonance and causes more photon absorption; its momentum kicks point against $v$. The beam travelling towards positive $x$ is further from resonance. Reversing $v$ exchanges the roles of the two beams. This is a Doppler effect on the absorption rates, rather than a need to follow each atom with a moving beam.

An absorbed photon gives the atom momentum $\pm \hslash k$, where $\hslash =h/(2\pi )$ is the reduced Planck constant. Subsequent spontaneous emission gives a recoil opposite to the emitted photon. For an emission pattern symmetric under reversal of direction, that recoil has zero expected value, but nonzero variance. Its mean therefore leaves the directional absorption force intact, while its randomness adds recoil energy. Random absorption times and choices of beam add further momentum fluctuations. Zero mean recoil does not mean zero heating.

A helium-neon laser uses an electrical discharge in a helium–neon gas mixture to establish optical gain on neon transitions. Excited helium atoms transfer energy in collisions to suitable excited neon states, helping create the required population inversion.

A discharge tube and aligned resonator mirrors form the apparatus; one mirror transmits part of the circulating light as output. The familiar red emission is one selected transition rather than a direct photograph of the entire discharge spectrum. Gas pressure, discharge current and mirror selection affect oscillation, while misalignment can suppress the laser output even when the discharge remains visibly bright.

Laser detuning is the difference between a laser angular frequency and a specified atomic resonance angular frequency. Red detuning has $\delta <0$; blue detuning has $\delta >0$. The sign matters in laser cooling: a moving atom can see a red-detuned laser closer to resonance because of the Doppler effect.

A shadow price measures the marginal improvement from relaxing an inequality optimization constraint. Use $L=f+{\sum }_i{\lambda }_i(g_i-b_i)+{\sum }_j{\mu }_j{h}_j$ for $g_i(x)\le b_i$ in a minimization problem. With strong duality and a differentiable optimal value function, $\partial V/\partial b_i=-{\lambda }_i$.^[ref]

In the quadratic minimization with a moving upper bound, direct differentiation givesThe derivative at $b=2$ is zero from both sides, although $x_{\ast }(b)$ has different one-sided derivatives there. The value of additional room has faded continuously to zero just as the active constraint stops restricting the optimizer.

At $b=1$, $x_{\ast }=1$, $V=1$ and ${\lambda }_{\ast }=2$. Raising the bound to $1.1$ lowers the value to $0.81$: the first-order prediction is a change of $-2(0.1)=-0.2$, while the exact change is $-0.19$. At $b=3$, the same relaxation leaves $x_{\ast }=2$ and $V=0$. At $b=2$, tightening by $0.1$ costs $0.01$, a second-order effect despite the zero Lagrange multiplier.

There is also a global statement in this example. Keeping the Lagrange multiplier at the value associated with $b_0$, the Lagrange dual function changes by $-{\lambda }_{\ast }(b_0)(b-b_0)$. Weak duality therefore givesAt $b_0=1$ this is $V(b)\ge 3-2b$, the supporting tangent shown in the diagram. This bound remains valid for large changes, whereas the derivative predicts only the first-order effect. If the value function is not differentiable, an optimal Lagrange multiplier can instead supply a supporting subgradient, rather than a unique marginal rate. This is the same idea used in sensitivity analysis in convex perturbation duality.

Consider the minimization problemThe objective function is a strictly convex function with its unconstrained optimization minimum at $x=2$. If $b<2$, it decreases throughout the feasible set until the bound $x=b$ is reached. If $b\ge 2$, the feasible set already contains $2$. HenceWith $g(x)=x-b$, the optimization Lagrangian and KKT conditions areSubstituting $x_{\ast }(b)$ gives the unique inequality Lagrange multiplierFor $b<2$, the active constraint has ${\lambda }_{\ast }>0$. For $b>2$, there is strict slack $b-x_{\ast }=b-2$ and ${\lambda }_{\ast }=0$. At $b=2$, the active constraint has zero Lagrange multiplier: the boundary happens to pass through the unconstrained optimum. Feasibility and complementary slackness must both be checked; solving only $2(x-2)+\lambda =0$ would allow infeasible candidates.

An independent certificate follows by completing the square:The Lagrange dual function is a concave function of $\lambda$. Its maximum over $\lambda \ge 0$ occurs at ${\lambda }_{\ast }(b)$ and equals $V(b)$. Thus every case has an explicit lower bound attained by a feasible point; the KKT conditions provide a certificate that can be checked without searching all real numbers.

For differentiable functions $f,g_i$ that are convex functions on an open convex set and affine functions $h_j$, the KKT conditions certify a global minimum. Once Lagrange multipliers satisfy them, no extra Slater condition is needed.^[ref]

The certificate is a short chain of inequalities. Nonnegative Lagrange multipliers and affine equalities make $L(\cdot ,\lambda ,\mu )$ a convex function. Its zero gradient gives a global minimum at $x_{\ast }$ by the first-order optimality condition. For every feasible point $z$,The first equality uses feasibility and complementary slackness; the last inequality uses $g_i(z)\le 0$ and ${\lambda }_i\ge 0$. Equivalently, the Lagrange dual function attains the same value as the primal objective function, proving strong duality. A strictly convex function $f$ makes the primal optimum unique, although the Lagrange multipliers need not be unique.

Affine equality optimization constraints matter: an arbitrary signed multiple of a nonlinear equality can destroy convexity of the optimization Lagrangian. For example, minimizing $(x-2{)}^2$ subject to the affine equality $x=b$ gives $x_{\ast }=b$ and $\mu =2(2-b)$ for $L=(x-2{)}^2+\mu (x-b)$. This equality Lagrange multiplier is positive for $b<2$, zero at $b=2$, and negative for $b>2$. Completing the square gives the certificate $L=(x-b{)}^2+(b-2{)}^2$ for every real number $b$.

Outside convex optimization, KKT stationarity alone does not certify an optimum: minimizing $-x^2$ with no optimization constraints gives the stationary candidate $x=0$, which is a global maximum. The Lagrangian sufficiency theorem remains applicable whenever a global minimum of the optimization Lagrangian can be proved directly.

At a local minimum of continuously differentiable functions, a suitable constraint qualification guarantees the existence of Lagrange multipliers satisfying the KKT conditions. One sufficient local regularity assumption is that the gradients of the equality optimization constraints and the active constraints are jointly linearly independent. This assumption concerns the description of the feasible set, not just the smoothness of the objective function.^[ref]

To see why a hypothesis is needed, minimize $f(x)=x$ subject to $g(x)=x^2\le 0$. The feasible set is the singleton $\{0\}$, so $x_{\ast }=0$ is a global minimum. Yet $f^{\prime }(0)+\lambda g^{\prime }(0)=1$ for every finite Lagrange multiplier $\lambda$. Thus this convex optimization problem has an attained optimum without a KKT multiplier. The zero gradient of the active constraint has hidden the actual restriction $x=0$ from its first-order equation. Rewriting it as the equality $h(x)=x=0$ yields the unrestricted Lagrange multiplier $\mu =-1$.

For convex optimization, Slater's condition requires strict inequalities and affine equalities at a point in the relative interior of the common domain of a function. A finite optimum value then gives strong duality and dual attainment. Primal attainment and differentiability yield KKT multipliers. Affine inequalities may be nonstrict in the refined version.^[ref]

Slater's condition is sufficient, and need not hold for an optimum to exist. The singular example above also shows that mere primal attainment is not a substitute for a constraint qualification.