Inverse and implicit function theorems
Turn invertible derivative information into local coordinates, while keeping global injectivity and graph choices separate.
On this page
The question for today
What you’ll understand
- State the local inverse theorem with a differentiability and rank hypothesis.
- Choose dependent variables using an invertible partial derivative block.
- Distinguish a local inverse or graph from a global one.
A derivative is linear information at one point, while an inverse is a statement about nearby actual values. The inverse function theorem connects them under a regularity hypothesis. The implicit theorem uses the same idea to turn equations into local graphs after choosing an invertible derivative block. Both theorems are local. A map can have a valid inverse near one point and still send distant points to the same target, while an equation can be a smooth curve even where one particular graph choice fails.
Invertibility at one point controls a neighborhood
Let F be continuously differentiable on an open subset of real n-space, and suppose its derivative at p is an invertible n-by-n matrix. There are open neighborhoods of p and F(p) on which F is a continuously differentiable diffeomorphism. If F is smooth, its local inverse is smooth. The inverse derivative is obtained by differentiating the identity, not by taking coordinatewise reciprocals of Jacobian entries. Continuity of the derivative is a convenient sufficient regularity assumption that controls its nearby behavior.
For maps of manifolds of equal dimension, use source and target charts and test whether the intrinsic tangent map is an isomorphism. Compatible chart changes preserve this condition. The result says nothing about global injectivity, the size of the inverse neighborhood, or how well conditioned numerical inversion is. A determinant can be nonzero but extremely small, making the local inverse sensitive. Conversely, failure of this sufficient test does not imply that the map has no set-theoretic inverse, only that this smooth-inverse conclusion is unavailable.
An equation becomes a graph after choosing a block
Suppose G maps an open subset of real (m+k)-space to real k-space, is continuously differentiable, and vanishes at (a,b). Split variables into x and y, with y having k coordinates. If the k-by-k derivative with respect to y is invertible at that point, nearby solutions are exactly y=g(x) for one continuously differentiable function. The theorem uses a chosen block; another block may work when this one fails. Differentiating G(x,g(x))=0 supplies the graph derivative.
This is a coordinate statement about a local level set, not a license to select one formula globally. A circle has upper and lower branches over much of the horizontal axis, and near its left and right extremes it is better expressed with the horizontal coordinate dependent on the vertical one. The geometry can stay smooth while the original graph representation breaks down. Listing which variables are solved for and where their derivative block is invertible prevents a coordinate limitation from being confused with a singular manifold.
Local normal forms organize later rank arguments
The inverse theorem can also complete selected independent coordinates to a full local coordinate system. This leads to normal forms for constant-rank maps and the regular-value theorem. A full-rank equation system cuts out the expected number of independent directions because coordinates can straighten those equations into projections. The difficult part is not merely counting equations but verifying that their differentials are independent at every relevant point of the level set.
Boundary points need separate treatment. An ordinary Euclidean local inverse theorem uses open neighborhoods, whereas a manifold boundary uses half-space charts and imposes compatibility with the boundary. Later preimage results will require the restriction to the boundary to satisfy its own rank or transversality condition. Until then, use the theorems here at interior points. Keep the analytic regularity, derivative block, coordinate domain, and locality visible when transferring a formula into a geometric claim.
Work it through
A local inverse despite a repeated distant value
For , examine p=(1,0). Find the derivative of its local inverse at F(p), and determine whether F is globally injective.
The target value is F(1,0)=(2,−1). The Jacobian at p is with determinant 4, so the smooth inverse theorem applies locally.
Invert the two-by-two matrix to obtain . Multiplying by the original Jacobian gives identity in either order.
However, F(−3,−4)=(−3−4+9,−4+3)=(2,−1) as well. This second source point lies outside a sufficiently small inverse neighborhood around p and does not contradict local invertibility.
A smooth local inverse exists near (1,0), with the displayed inverse derivative. The map is not globally injective, so there is no single global inverse on its whole image.
The living notebook
Explore the model
Interactive lab · matrix
Inspect conditioning within the invertible regime
Pause and Reset; set a=1,b=0 and predict condition number 1. For a fresh comparison Reset and set a=0.25,b=1: predict a smaller minimum singular value and greater inverse sensitivity. Positive a keeps this family invertible throughout the controls. The instrument illustrates derivative matrices, not the size of a nonlinear inverse neighborhood or a proof of global injectivity.
- Area scale · det A
- 1.4
- Singular values · σmax, σmin
- 1.597, 0.876
- Condition number · κ₂(A)
- 1.823
The selected matrix is A = [[a,b],[0,1]]. Playback interpolates geometry between the identity and A; readouts always describe A. Its determinant is a and its singular values come from AᵀA. Positive a makes every supported matrix invertible. This two-dimensional family is not a general eigensolver or a learned network. Parameter changes pause playback and recompute the model at the current elapsed time. Reset restores both parameters and returns time to zero. Playback starts paused. Adjust either parameter, then choose Play or Step.
Look forNonzero determinant gives a local theorem, while singular values quantify sensitivity and global behavior needs additional arguments.
Pause & reason
Check your understanding
Your turn
Try it yourself
Near (3,1), express as y=g(x) and calculate g′(3). Why does the same graph test fail at (√13,0), even though the ellipse remains smooth?
Take a moment to make a prediction or work it out on paper. Open a hint when you need one.
Give me a hint
For G=x²+4y²−13, inspect G_y. If it vanishes, inspect G_x before declaring the level set singular.
Compare with the solution
At (3,1), G_y=8 is nonzero, so the positive branch is smooth nearby and g′(3)=−G_x/G_y=−6/8=−3/4. At (√13,0), G_y=0, so this y-as-a-function-of-x test fails. But G_x=2√13 is nonzero, allowing x to be solved smoothly as a function of y. The curve is regular; only the graph direction changes.
Keep the idea
Take it with you
- The inverse theorem is local and requires an invertible derivative with appropriate regularity.
- The implicit theorem solves for a selected invertible derivative block.
- A failed graph choice need not indicate a singular geometric level set.
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