Scientific Computing

in Lean

Tomáš Skřivan

Carnegie Mellon University

Hoskinson Center for Formal Mathematics

9.5.2024

Why Lean

for Scientific Computing?

The language of science is mathematics

... and Lean speaks it!

 

Something interesting has to come out of it!

m \ddot x_i = \sum_j G \frac{m_i m_j}{r_{ij}^2}\hat r_{ij}
\begin{align*} v_i^{n+1} &= v_i^n + \Delta t \, \sum_j F(x_i^n,x_j^n) \\ x_i^{n+1} &= x_i^n + \Delta t \, v_i^{n+1} \end{align*}
def update (x v : Array Vec3) :=
  v := v + Δt * force x
  x := x + Δt * v
  (x,v)

+

+

+

Why Lean?

  • mathematical abstraction
    • generality, composability
    • seamless mix of symbolic and numerical methods
  • interactivity and (ab)use of tactic mode
    • computer algebra system/source code transformations
    • specification to implementation
    • code optimization
  • extensible syntax
  • formal correctness

Examples demonstrating

usefulness of Lean

  • symbolic/automatic differentiation
    • general framework for function transformation
  • synthesizing approximations
    • specification to implementation
  • array expression optimization
    • ​interactive code optimization
  • probabilistic programs

Symbolic and automatic

differentiation

def foo (x : R) : R := x^2
def foo_deriv (x : R) : R := 2*x

AD

  • automatic differentiation (AD) synthesizes derivatives of a program
  • application in machine learning, optimization, and scientific computing

Symbolic and automatic

differentiation

def foo (x : R) : R := x^2
noncomputable
def foo_deriv (x : R) : R := fderiv R (fun x' => foo x') x 1
noncomputable
def fderiv (f : X → Y) (x : X) : X →L[𝕜] Y :=
  if h : ∃ (f' : X →L[𝕜] Y), (fun x' => f x' - f x - f' (x' - x)) =o[𝓝 x] fun x' => x' - x then
    choose h
  else 
  	0
  • definition of derivative in mathlib
  • using mathlib derivative
def foo_deriv (x : R) : R := (fderiv R (fun x' => foo x') x 1) rewrite_by fun_trans
  • fun_trans is a general tactic for function transformation

General function transformation

  • setting up fderiv as function transformation
  • definition:
@[fun_trans]
noncomputable
def fderiv (f : X → Y) (x : X) : X →L[𝕜] Y := ...
@[fun_trans]
theorem fderiv_add :
    (fderiv K fun x => f x + g x)
    =
    fun x => fderiv K f x + fderiv K g x := ...
@[fun_trans]
theorem fderiv_comp :
    fderiv 𝕜 (fun x => (f (g x))) 
    = 
    fun x => fun dx =>L[K] fderiv K f (g x) (fderiv K g x dx) := ...
@[fun_trans]
theorem fderiv_id :
    (fderiv K fun x : X => x) = fun _ => fun dx =>L[K] dx := ...
  • transformation rules:
  • code demo

Synthesizing approximations

noncomputable
def sqrt (y : R) : R := (fun x => x^2)⁻¹ y
def mySqrt (y : R) : Approx P (sqrt y) := ...
  • many operations line solving system of equations, integrals, solving ODEs etc. can not be computed exactly
  • we need a way to construct approximations
  • example of constructing approximation of sqrt 
#check mySqrt.is_approx  -- "∀ y, sqrt y = limit p, mySqrt p y"
#check P -- Filter ApproxParam

#eval mySqrt {absTol := 1e-9} 2.0  -- 1.414214
#eval mySqrt {maxSteps := 1}  2.0  -- 1.5
  • ideally Approx provides theorem
  • code demo

Array expression optimization

  • custom interactive optimizations
  • example on basic linear algebra operation saxpy 
def saxpy (a : Float) (x y : Float^[n]) := a•x+y
def saxpy_optimized (a : Float) (x y : Float^[n]) := x.mapIdx (fun i xi => a*xi + y[i])

?

def_optimize saxpy by
  simp only [HAdd.hAdd,Add.add,HSMul.hSMul,SMul.smul]
  simp only [mapMono_mapIdxMono]
  • semi-automatic with def_optimize macro
  • code demo

Probabilistic programming

structure Rand (X : Type) [MeasurableSpace X] where
  μ : Erased (Measure X)
  is_prob : IsProbabilityMeasure μ.out
  rand : RandG StdGen X
  • definition of random variable
  • for x : Rand X
    • ​x.μ and x.is_prob is used for static analysis
    • x.rand is pseudo random generator used by the compiler to emit executable code
  • example of probabilistic program
def test (θ : R) : Rand R := do
  let b ← flip θ
  if b then
    pure 0
  else
    pure (-θ/2)

Solving Laplace equation and

Walk on Spheres

\begin{align*} \Delta u(x) &= 0 \qquad &x \in \Omega \\ u(x) &= g(x) \qquad &x \in \partial \Omega \end{align*}
  • Laplace equation
  • mean value property
\begin{align*} u(x) &= \frac{1}{4\pi r^2} \int_{S(x,r)} u(y) \,dy \qquad B(x,r) \subset \Omega \end{align*}
  • recursive definition
\begin{align*} u_n(x) &= \frac{1}{4\pi r_n^2} \int_{S(x,r_n)} u_{n-1}(y) \,dy \\ r_n &= \text{dist}(x_n,\partial \Omega) \\ u_0(x) &= g(x) \end{align*}
  • apply Monte Carlo method
\frac{1}{4\pi r_n^2} \int_{S(x,r_n)} u_{n-1}(y) \,dy = \mathbb{E}_{y\sim P}[u_{n-1}(y)]
u(x) = u_\infty(x)

Mathlib limitations 

  • many standard mathematical notions are to rigid for reasoning about programs
  • reasoning about programs needs easy to use calculus and not strong existence guarantees

mathematics

programs

  • Banach spaces
  • manifolds
  • strong/Bochner integral
  • measurable spaces
  • Convenient vector spaces
  • diffeology
  • weak/Pettis integral
  • quasi-Borel spaces

important properties

completeness

compactness

better behavior of function spaces e.g. cartesian closed category

SciLean

  • website: https://github.com/lecopivo/SciLean
  • work in progress book/documentation: https://github.com/lecopivo/scientific-computing-lean/
    • only first chapter on using arrays
  • design principles of SciLean
    1. usability, composability and generality
    2. performance
    3. formal correctness​
  • Lean is an amazing new language and many of its features designed for theorem proving offer a new and exciting design space for a scientific computing language
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