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sshin23 merged 4 commits into from
Aug 3, 2026
Merged

T-aware overloads for collocation/control and PDE problems #14

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afb378a
T-aware overloads for 9 collocation/control problems
sshin23 May 18, 2026
3d4b826
Remove [sources] ExaModels entry — Julia 1.12 rejects sources without…
sshin23 May 19, 2026
09258b5
pde_models: T-aware constants for fp32 GPU (dirichlet/henon/lane_emden)
sshin23 May 20, 2026
81a6a8b
test: qualify NLPModels accessors shadowed by ExaModels exports
sshin23 Aug 3, 2026
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3 changes: 0 additions & 3 deletions Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -14,9 +14,6 @@ ExaModels = "1037b233-b668-4ce9-9b63-f9f681f55dd2"
COPSBenchmarkExaModels = "ExaModels"
COPSBenchmarkJuMP = "JuMP"

[sources]
ExaModels = {url = "https://github.com/exanauts/ExaModels.jl", rev = "main"}

[compat]
JuMP = "^1.19"
julia = "1.11"
42 changes: 21 additions & 21 deletions ext/COPSBenchmarkExaModels/catmix.jl
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Expand Up @@ -7,65 +7,67 @@
ne = 2
nc = 3

tf = 1
h = tf / nh # Final time
h = T(1) / T(nh) # Final time / nh (was Int/Int → Float64)

rho = [
rho = T[
0.11270166537926,
0.50000000000000,
0.88729833462074,
]
bc = [1.0, 0.0] # Boundary conditions for x
alpha = 0.0 # Smoothing parameter
bc = T[1, 0] # Boundary conditions for x
alpha = T(0) # Smoothing parameter
neg_one = -T(1)
ten_T = T(10)
one_T = T(1)
rho_index = [(i, rho[i]) for i in 1:nc]

c = ExaModels.ExaCore(T; backend = backend, concrete = Val(true))
ExaModels.@add_var(c, u, nh, nc; lvar = zeros(nh, nc), uvar = ones(nh, nc), start = zeros(nh, nc))
ExaModels.@add_var(c, v, nh, ne; start = [mod(j, ne) for i in 1:nh, j in 1:ne])
ExaModels.@add_var(c, w, nh, nc, ne; start = zeros(nh, nc, ne))
ExaModels.@add_var(c, pp, nh, nc, ne; start = [mod(k, ne) for i in 1:nh, j in 1:nc, k in 1:ne])
ExaModels.@add_var(c, Dpp, nh, nc, ne; start = zeros(nh, nc, ne))
ExaModels.@add_var(c, ppf, ne; start = [mod(i,ne) for i in 1:ne])

ExaModels.@add_obj(c, -1.0 + ppf[1] + ppf[2])
ExaModels.@add_var(c, u, nh, nc; lvar = zeros(T, nh, nc), uvar = ones(T, nh, nc), start = zeros(T, nh, nc))
ExaModels.@add_var(c, v, nh, ne; start = T[mod(j, ne) for i in 1:nh, j in 1:ne])
ExaModels.@add_var(c, w, nh, nc, ne; start = zeros(T, nh, nc, ne))
ExaModels.@add_var(c, pp, nh, nc, ne; start = T[mod(k, ne) for i in 1:nh, j in 1:nc, k in 1:ne])
ExaModels.@add_var(c, Dpp, nh, nc, ne; start = zeros(T, nh, nc, ne))
ExaModels.@add_var(c, ppf, ne; start = T[mod(i,ne) for i in 1:ne])

ExaModels.@add_obj(c, neg_one + ppf[1] + ppf[2])
ExaModels.@add_obj(c, alpha/h*(u[i+1, j] - u[i, j])^2 for i in 1:nh-1, j in 1:nc)

ExaModels.@add_con(
c,
c1,
pp[i, k, s] - v[i, s] - h*sum(w[i, j, s]*(rho^j/factorial(j)) for j in 1:nc) for i=1:nh, (k, rho) in rho_index, s=1:ne
pp[i, k, s] - v[i, s] - h*sum(w[i, j, s]*(rho^j/T(factorial(j))) for j in 1:nc) for i=1:nh, (k, rho) in rho_index, s=1:ne
)

ExaModels.@add_con(
c,
c2,
Dpp[i, k, s] - sum(w[i, j, s]*(rho^(j-1)/factorial(j-1)) for j in 1:nc) for i=1:nh, (k, rho) in rho_index, s=1:ne
Dpp[i, k, s] - sum(w[i, j, s]*(rho^(j-1)/T(factorial(j-1))) for j in 1:nc) for i=1:nh, (k, rho) in rho_index, s=1:ne
)

ExaModels.@add_con(
c,
c3,
ppf[s] - v[nh, s] - h * sum(w[nh, j, s] / factorial(j) for j in 1:nc) for s in 1:ne
ppf[s] - v[nh, s] - h * sum(w[nh, j, s] / T(factorial(j)) for j in 1:nc) for s in 1:ne
)

ExaModels.@add_con(
c,
c4,
v[i, s] + sum(w[i, j, s] * h / factorial(j) for j in 1:nc) - v[i+1, s] for i in 1:nh-1, s in 1:ne
v[i, s] + sum(w[i, j, s] * h / T(factorial(j)) for j in 1:nc) - v[i+1, s] for i in 1:nh-1, s in 1:ne
)



ExaModels.@add_con(
c,
c5,
Dpp[i,j,1] - u[i,j] * (10.0*pp[i,j,2] - pp[i,j,1]) for i=1:nh, j=1:nc
Dpp[i,j,1] - u[i,j] * (ten_T*pp[i,j,2] - pp[i,j,1]) for i=1:nh, j=1:nc
)

ExaModels.@add_con(
c,
c6,
Dpp[i,j,2] - u[i,j] * (pp[i,j,1] - 10.0*pp[i,j,2]) + (1 - u[i,j])*pp[i,j,2] for i=1:nh, j=1:nc
Dpp[i,j,2] - u[i,j] * (pp[i,j,1] - ten_T*pp[i,j,2]) + (one_T - u[i,j])*pp[i,j,2] for i=1:nh, j=1:nc
)


Expand All @@ -77,5 +79,3 @@

return ExaModels.ExaModel(c; kwargs...)
end


30 changes: 15 additions & 15 deletions ext/COPSBenchmarkExaModels/gasoil.jl
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Expand Up @@ -12,18 +12,19 @@
nm = 21 # number of measurements

# roots of k-th degree Legendre polynomial
rho = [0.06943184420297, 0.33000947820757, 0.66999052179243, 0.93056815579703]
rho = T[0.06943184420297, 0.33000947820757, 0.66999052179243, 0.93056815579703]
# ODE initial conditions
bc = [1, 1, 2, 0]
# times at which observations made
tau = [0.0, 0.025, 0.05, 0.075, 0.10, 0.125, 0.150, 0.175, 0.20, 0.225, 0.250, 0.30, 0.35, 0.40, 0.45, 0.50, 0.55, 0.65, 0.75, 0.85, 0.95]
tau = T[0.0, 0.025, 0.05, 0.075, 0.10, 0.125, 0.150, 0.175, 0.20, 0.225, 0.250, 0.30, 0.35, 0.40, 0.45, 0.50, 0.55, 0.65, 0.75, 0.85, 0.95]
# ODEs defined in [0,tf]
tf = tau[nm]
# uniform interval length
h = tf / nh
t = [(i-1)*h for i in 1:nh+1]
h = tf / T(nh)
t = T[T(i-1)*h for i in 1:nh+1]
zero_T = T(0)

itau = Int[min(nh, floor(tau[i]/h)+1) for i in 1:nm]
itau = Int[min(nh, Int(floor(tau[i]/h))+1) for i in 1:nm]

# Concentrations
z = reshape(T[
Expand All @@ -50,10 +51,10 @@
0.0690, 0.0100,
], ne, nm)'

v0 = zeros(nh, ne)
v0 = zeros(T, nh, ne)
# Starting-value
for i in 1:itau[1], s in 1:ne
v0[i, s] = bc[s]
v0[i, s] = T(bc[s])
end
for j in 2:nm, i =itau[j-1]+1:itau[j], s in 1:ne
v0[i, s] = z[j, s]
Expand All @@ -65,31 +66,31 @@
core = ExaModels.ExaCore(T; backend= backend, concrete = Val(true))

# ODE parameters
ExaModels.@add_var(core, theta, 1:np; lvar = 0.0, start=0.0)
ExaModels.@add_var(core, theta, 1:np; lvar = zero_T, start=zero_T)
# The collocation approximation u is defined by the parameters v and w.
# uc and Duc are, respectively, u and u' evaluated at the collocation points.
ExaModels.@add_var(core, v, 1:nh, 1:ne; start=[v0[i, s] for i =1:nh, s = 1:ne])
ExaModels.@add_var(core, w, 1:nh, 1:nc, 1:ne; start=0.0)
ExaModels.@add_var(core, w, 1:nh, 1:nc, 1:ne; start=zero_T)
ExaModels.@add_var(core, uc, 1:nh, 1:nc, 1:ne; start=[v0[i, s] for i =1:nh, j=1:nc, s = 1:ne])
ExaModels.@add_var(core, Duc, 1:nh, 1:nc, 1:ne; start=0.0)
ExaModels.@add_var(core, Duc, 1:nh, 1:nc, 1:ne; start=zero_T)

itr = [(j, s, itau[j], tau[j], t[itau[j]], z[j,s]) for j=1:nm, s in 1:ne]
itr2 = [(j,rho[j]) for j=1:nc]

# L2 error
ExaModels.@add_obj(core, (v[itauj,s] + sum(w[itauj,k,s]*(tauj-tj)^k/(factorial(k)*h^(k-1)) for k in 1:nc) - zjs)^2 for (j,s,itauj, tauj, tj, zjs) in itr)
ExaModels.@add_obj(core, (v[itauj,s] + sum(w[itauj,k,s]*(tauj-tj)^k/(T(factorial(k))*h^(k-1)) for k in 1:nc) - zjs)^2 for (j,s,itauj, tauj, tj, zjs) in itr)

# Collocation model
ExaModels.@add_con(
core,
c1,
- uc[i, j, s] + v[i,s] + h*sum(w[i,k,s]*(rhoj^k/factorial(k)) for k in 1:nc)
- uc[i, j, s] + v[i,s] + h*sum(w[i,k,s]*(rhoj^k/T(factorial(k))) for k in 1:nc)
for i=1:nh, (j,rhoj) in itr2, s=1:ne
)
ExaModels.@add_con(
core,
c2,
- Duc[i, j, s] + sum(w[i,k,s]*(rhoj^(k-1)/factorial(k-1)) for k in 1:nc)
- Duc[i, j, s] + sum(w[i,k,s]*(rhoj^(k-1)/T(factorial(k-1))) for k in 1:nc)
for i=1:nh, (j,rhoj) in itr2, s=1:ne
)

Expand All @@ -100,7 +101,7 @@
ExaModels.@add_con(
core,
c4,
v[i, s] + sum(w[i, j, s]*h/factorial(j) for j in 1:nc) - v[i+1, s]
v[i, s] + sum(w[i, j, s]*h/T(factorial(j)) for j in 1:nc) - v[i+1, s]
for i=1:nh-1, s=1:ne
)
ExaModels.@add_con(
Expand All @@ -117,4 +118,3 @@
)
return ExaModels.ExaModel(core; kwargs...)
end

66 changes: 35 additions & 31 deletions ext/COPSBenchmarkExaModels/glider.jl
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Expand Up @@ -6,52 +6,56 @@
# COPS 3.1 - March 2004

@inline function COPSBenchmark.glider_model(::ExaModelsBackend, nh; T = Float64, backend = nothing, kwargs...)
# Design parameters
x_0 = 0.0
y_0 = 1000.0
y_f = 900.0
vx_0 = 13.23
vx_f = 13.23
vy_0 = -1.288
vy_f = -1.288
u_c = 2.5
r_0 = 100.0
m = 100.0
g = 9.81
cd0 = 0.034
cd1 = 0.069662
S = 14.0
rho = 1.13
cL_min = 0.0
cL_max = 1.4
cL0 = cL_max / 2
# Design parameters (T-typed)
x_0 = T(0)
y_0 = T(1000)
y_f = T(900)
vx_0 = T(13.23)
vx_f = T(13.23)
vy_0 = T(-1.288)
vy_f = T(-1.288)
u_c = T(2.5)
r_0 = T(100)
m = T(100)
g = T(9.81)
cd0 = T(0.034)
cd1 = T(0.069662)
S = T(14)
rho = T(1.13)
cL_min = T(0)
cL_max = T(1.4)
cL0 = cL_max / T(2)
half = T(0.5)
inv_nh = T(1) / T(nh)
r_offset = T(2.5)
one_T = T(1)

c = ExaModels.ExaCore(T; backend = backend, concrete = Val(true))

ExaModels.@add_var(c, t_f, 1; lvar = 0.0, start = 1.0)
ExaModels.@add_var(c, x, nh+1; lvar = zeros(nh+1), start = [x_0 + vx_0*(k/nh) for k in 0:nh])
ExaModels.@add_var(c, y, nh+1; start = [y_0 + (k/nh)*(y_f - y_0) for k in 0:nh])
ExaModels.@add_var(c, vx, nh+1; lvar = zeros(nh+1), start = fill(vx_0, nh+1))
ExaModels.@add_var(c, t_f, 1; lvar = T(0), start = T(1))
ExaModels.@add_var(c, x, nh+1; lvar = zeros(T, nh+1), start = [x_0 + vx_0*(T(k)*inv_nh) for k in 0:nh])
ExaModels.@add_var(c, y, nh+1; start = [y_0 + (T(k)*inv_nh)*(y_f - y_0) for k in 0:nh])
ExaModels.@add_var(c, vx, nh+1; lvar = zeros(T, nh+1), start = fill(vx_0, nh+1))
ExaModels.@add_var(c, vy, nh+1; start = fill(vy_0, nh+1))
ExaModels.@add_var(c, cL, nh+1; lvar = fill(cL_min,nh+1), uvar = fill(cL_max, nh+1), start = fill(cL0, nh+1))

# Expressions (matching JuMP @expressions)
ExaModels.@add_expr(c, r, (x[i]/r_0 - 2.5)^2 for i in 1:nh+1)
ExaModels.@add_expr(c, u, u_c*(1 - r[i])*exp(-r[i]) for i in 1:nh+1)
ExaModels.@add_expr(c, r, (x[i]/r_0 - r_offset)^2 for i in 1:nh+1)
ExaModels.@add_expr(c, u, u_c*(one_T - r[i])*exp(-r[i]) for i in 1:nh+1)
ExaModels.@add_expr(c, w, vy[i] - u[i] for i in 1:nh+1)
ExaModels.@add_expr(c, v, sqrt(vx[i]^2 + w[i]^2) for i in 1:nh+1)
ExaModels.@add_expr(c, D, 0.5*(cd0+cd1*cL[i]^2)*rho*S*v[i]^2 for i in 1:nh+1)
ExaModels.@add_expr(c, L, 0.5*cL[i]*rho*S*v[i]^2 for i in 1:nh+1)
ExaModels.@add_expr(c, D, half*(cd0+cd1*cL[i]^2)*rho*S*v[i]^2 for i in 1:nh+1)
ExaModels.@add_expr(c, L, half*cL[i]*rho*S*v[i]^2 for i in 1:nh+1)
ExaModels.@add_expr(c, vx_dot, (-L[i]*(w[i]/v[i]) - D[i]*(vx[i]/v[i]))/m for i in 1:nh+1)
ExaModels.@add_expr(c, vy_dot, (L[i]*(vx[i]/v[i]) - D[i]*(w[i]/v[i]))/m - g for i in 1:nh+1)

ExaModels.@add_obj(c, -x[nh+1])

# Dynamics
ExaModels.@add_con(c, c1, x[j] - (x[j-1] + 0.5 * t_f[1]/nh * (vx[j] + vx[j-1])) for j in 2:nh+1)
ExaModels.@add_con(c, c2, y[j] - (y[j-1] + 0.5 * t_f[1]/nh * (vy[j] + vy[j-1])) for j in 2:nh+1)
ExaModels.@add_con(c, c3, vx[j] - (vx[j-1] + 0.5 * t_f[1]/nh * (vx_dot[j] + vx_dot[j-1])) for j in 2:nh+1)
ExaModels.@add_con(c, c4, vy[j] - (vy[j-1] + 0.5 * t_f[1]/nh * (vy_dot[j] + vy_dot[j-1])) for j in 2:nh+1)
ExaModels.@add_con(c, c1, x[j] - (x[j-1] + half * t_f[1]*inv_nh * (vx[j] + vx[j-1])) for j in 2:nh+1)
ExaModels.@add_con(c, c2, y[j] - (y[j-1] + half * t_f[1]*inv_nh * (vy[j] + vy[j-1])) for j in 2:nh+1)
ExaModels.@add_con(c, c3, vx[j] - (vx[j-1] + half * t_f[1]*inv_nh * (vx_dot[j] + vx_dot[j-1])) for j in 2:nh+1)
ExaModels.@add_con(c, c4, vy[j] - (vy[j-1] + half * t_f[1]*inv_nh * (vy_dot[j] + vy_dot[j-1])) for j in 2:nh+1)

# Boundary constraints
ExaModels.@add_con(c, c5, x[1] - x_0)
Expand Down
33 changes: 16 additions & 17 deletions ext/COPSBenchmarkExaModels/marine.jl
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Expand Up @@ -10,14 +10,15 @@
ne = 8 # number of differential equations
nm = 21 # number of measurements

rho = [0.5] # roots of k-th degree Legendre polynomial
tau = collect(range(0.0, 10.0, 21)) # times at which observations made
tf = tau[nm] # ODEs defined in [0,tf]
h = tf / nh # uniform interval length
t = [(i-1)*h for i in 1:nh+1] # partition
rho = T[0.5] # roots of k-th degree Legendre polynomial
tau = collect(T, range(T(0), T(10), 21)) # times at which observations made
tf = tau[nm] # ODEs defined in [0,tf]
h = tf / T(nh) # uniform interval length
t = T[T(i-1)*h for i in 1:nh+1] # partition
zero_T = T(0)

# itau[i] is the largest integer k with t[k] <= tau[i]
itau = Int[min(nh, floor(tau[i]/h)+1) for i in 1:nm]
itau = Int[min(nh, Int(floor(tau[i]/h))+1) for i in 1:nm]

# Observation
z = reshape(T[
Expand All @@ -44,7 +45,7 @@
1.0, 12.0, 198.0, 707.0, 2562.0, 3163.0, 3232.0, 5566.0,
], ne, nm)'

v0 = zeros(nh, ne)
v0 = zeros(T, nh, ne)
# Starting-value
for i in 1:itau[1], s in 1:ne
v0[i, s] = z[1, s]
Expand All @@ -59,15 +60,15 @@
core = ExaModels.ExaCore(T; backend= backend, concrete = Val(true))

# Growth rates
ExaModels.@add_var(core, g, 1:ne-1; lvar = 0.0)
ExaModels.@add_var(core, g, 1:ne-1; lvar = zero_T)
# Mortality rates
ExaModels.@add_var(core, m, 1:ne; lvar = 0.0)
ExaModels.@add_var(core, m, 1:ne; lvar = zero_T)
# The collocation approximation u is defined by the parameters v and w.
# uc and Duc are, respectively, u and u' evaluated at the collocation points.
ExaModels.@add_var(core, v, 1:nh, 1:ne; start=[v0[i, s] for i=1:nh, s=1:ne])
ExaModels.@add_var(core, w, 1:nh, 1:nc, 1:ne; start=0.0)
ExaModels.@add_var(core, w, 1:nh, 1:nc, 1:ne; start=zero_T)
ExaModels.@add_var(core, uc, 1:nh, 1:nc, 1:ne; start=[v0[i, s] for i=1:nh, s=1:ne])
ExaModels.@add_var(core, Duc, 1:nh, 1:nc, 1:ne; start=0.0)
ExaModels.@add_var(core, Duc, 1:nh, 1:nc, 1:ne; start=zero_T)

# error

Expand All @@ -76,26 +77,26 @@
itr = [(j, s, itau[j], tau[j], t[itau[j]], z[j,s]) for j=1:nm, s in 1:ne]
itr2 = [(j,rho[j]) for j=1:nc]

ExaModels.@add_obj(core, (v[itauj,s] + sum(w[itauj,k,s]*(tauj-tj)^k/(factorial(k)*h^(k-1)) for k in 1:nc) - zjs)^2 for (j,s,itauj,tauj, tj, zjs) in itr)
ExaModels.@add_obj(core, (v[itauj,s] + sum(w[itauj,k,s]*(tauj-tj)^k/(T(factorial(k))*h^(k-1)) for k in 1:nc) - zjs)^2 for (j,s,itauj,tauj, tj, zjs) in itr)

# Collocation model
ExaModels.@add_con(
core,
c1,
- uc[i, j, s] + v[i,s] + h*sum(w[i,k,s]*(rhoj^k/factorial(k)) for k in 1:nc)
- uc[i, j, s] + v[i,s] + h*sum(w[i,k,s]*(rhoj^k/T(factorial(k))) for k in 1:nc)
for i=1:nh, (j,rhoj) in itr2, s=1:ne
)
ExaModels.@add_con(
core,
c2,
- Duc[i, j, s] + sum(w[i,k,s]*(rhoj^(k-1)/factorial(k-1)) for k in 1:nc)
- Duc[i, j, s] + sum(w[i,k,s]*(rhoj^(k-1)/T(factorial(k-1))) for k in 1:nc)
for i=1:nh, (j,rhoj) in itr2, s=1:ne
)
# Continuity
ExaModels.@add_con(
core,
c3,
v[i, s] + sum(w[i, j, s]*h/factorial(j) for j in 1:nc) - v[i+1, s]
v[i, s] + sum(w[i, j, s]*h/T(factorial(j)) for j in 1:nc) - v[i+1, s]
for i=1:nh-1, s=1:ne
)
# Boundary conditions
Expand All @@ -121,5 +122,3 @@

return ExaModels.ExaModel(core; kwargs...)
end


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