IB Maths AI HL Coupled & Second Order Differential Equations Paper 1 & 2 ~6 min read

Equilibrium Points

An equilibrium point is where the system stops moving: both dxdt = 0 and dydt = 0. A trajectory starting there never leaves. Whether nearby trajectories settle towards it (stable) or escape (unstable) is decided entirely by the eigenvalues of M.

πŸ“˜ What you need to know

Finding an equilibrium point

Equilibrium condition dxdt = 0  and  dydt = 0  simultaneously βœ“ standard definition for AI HL

🧠 “Both rates zero β€” nothing moves”

At equilibrium neither variable is changing, so a particle placed there stays put forever. To locate it, set both derivatives to zero and solve the pair of equations.

Origin guaranteed: for αΊ‹ = Mx (no constant terms), putting x = y = 0 makes both rates zero, so (0, 0) is always an equilibrium point. Constant terms shift it elsewhere.

Stable, unstable & the eigenvalue test

πŸ€” Why do the eigenvalues decide stability?

Near the origin the solution is built from eΞ»t terms. A negative eigenvalue makes its term shrink towards zero (pulling trajectories in); a positive one makes it grow (pushing them out). For complex eigenvalues the real part plays the same role. So the signs of the eigenvalues’ real parts tell you directly whether the point attracts or repels.

Eigenvalues of MNature of (0, 0)
Both positive, distinctUnstable
Both negative, distinctStable
One positive, one negativeUnstable (saddle)
Complex, positive real partsUnstable
Complex, negative real partsStable
Complex, zero real partsStable (a centre)

🧠 “Negative real parts pull in”

If every eigenvalue (or real part) is negative, the point is stable. If any is positive, it’s unstable. Purely imaginary eigenvalues give a stable centre of closed orbits.

🧭 Recipe β€” locate and classify

  1. Set both rates to 0 and solve for (x, y).
  2. Find the eigenvalues of M.
  3. Check the signs (or real parts).
  4. Classify using the table: all-negative β†’ stable; any positive β†’ unstable; mixed real β†’ saddle.
  5. Name it if asked (node, spiral, centre, saddle).

Worked examples

WE 1

Show that (3, 4) is an equilibrium point of dxdt = 2x βˆ’ 3y + 6, dydt = x + y βˆ’ 7.

Substitute and check both rates are zero.

dxdt = 2(3) βˆ’ 3(4) + 6 = 0 dydt = 3 + 4 βˆ’ 7 = 0 both zero at (3, 4), so it is an equilibrium point
WE 2

For dxdt = x + 3y, dydt = 2x + 2y (eigenvalues 4 and βˆ’1), find the coordinates and nature of the equilibrium point.

No constant terms, so the origin is the equilibrium; classify by the eigenvalues.

x = y = 0 makes both rates 0 β†’ equilibrium at (0, 0) eigenvalues 4 (> 0) and βˆ’1 (< 0): one each sign (0, 0) is a saddle point β€” unstable
WE 3

Find the equilibrium point of dxdt = 2x + 3y βˆ’ 8, dydt = 3x βˆ’ y βˆ’ 1.

Constant terms shift the equilibrium off the origin β€” solve simultaneously.

2x + 3y βˆ’ 8 = 0 and 3x βˆ’ y βˆ’ 1 = 0 from the second: y = 3x βˆ’ 1 2x + 3(3x βˆ’ 1) βˆ’ 8 = 0 β†’ 11x βˆ’ 11 = 0 β†’ x = 1 equilibrium point (1, 2)
WE 4

A system has eigenvalues βˆ’2 and βˆ’5. Classify the equilibrium at the origin.

Both real and negative.

both negative and distinct stable (a stable node)
WE 5

A system has complex eigenvalues 2 Β± 3i. Classify the equilibrium at the origin.

Complex β€” look at the real part.

real part = 2 > 0 spirals away from the origin unstable

πŸ’‘ Top tips

⚠ Common mistakes

Next up β€” Sketching Solution Trajectories. You can now locate an equilibrium point and judge whether it attracts or repels. The next topic draws the one trajectory a given start point follows: mark the initial point, find the initial direction from dxdt and dydt at t = 0, then steer it using the eigenvector lines and the stability you’ve just classified.

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