# JuMP memory allocation
**URL:** https://discourse.julialang.org/t/jump-memory-allocation/126731
**Category:** Optimization (Mathematical)
**Tags:** jump, optimization
**Created:** [March 9, 2025, 4:26pm UTC](https://discourse.julialang.org/t/jump-memory-allocation/126731 "2025-03-09T16:26:51Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@fdekerme](https://discourse.julialang.org/u/fdekerme)
#### Post date: [March 9, 2025, 4:26pm UTC](https://discourse.julialang.org/t/jump-memory-allocation/126731/1 "2025-03-09T16:26:52Z")
Hi! 😀
I am starting to use JuMP.jl to implement an optimization process on an image (size 266 x 353 pixels). I implement the problem like that to find L₁, L₂ values on each pixel:
```julia
model = Model(Ipopt.Optimizer)
set_silent(model)
@variable(model, 0 <= L₁[i = 1:x, j = 1:y], start = start_values.mat1[i, j])
@variable(model, 0 <= L₂[i = 1:x, j = 1:y], start = start_values.mat2[i, j])
function f(spectra, L₁, L₂)
sum((detector_response(spectra.top[i,j, :], L₁[i,j], L₂[i,j]) - top_image[i, j])^2 +
(detector_response(spectra.bottom[i,j,:], L₁[i,j], L₂[i,j]) - bottom_image[i, j])^2
for i in 1:x, j in 1:y)
end
@objective(model, Min, f(spectra, L₁, L₂))
optimize!(model)
```
However, this process takes up an enormous amount of RAM (more than 50GB) at the `@objective(model, Min, f(spectra, L₁, L₂))` stage. What am I doing wrong and could I improve to reduce RAM usage?
Thanks,
fdekerm
---
### Author:  [@BdeKoning](https://discourse.julialang.org/u/BdeKoning)
#### Post date: [March 9, 2025, 4:30pm UTC](https://discourse.julialang.org/t/jump-memory-allocation/126731/2 "2025-03-09T16:30:05Z")
Slicing allocates by default in Julia. Try views: `view(spectra.top, i, j, :)`
---
### Author:  [@odow](https://discourse.julialang.org/u/odow)
#### Post date: [March 9, 2025, 11:15pm UTC](https://discourse.julialang.org/t/jump-memory-allocation/126731/3 "2025-03-09T23:15:51Z")
Hi @fdekerme, I’ve moved this to the “Optimization (Mathematical)” section.
Can you provide a reproducible example?
The answer depends one what `detector_response` is doing. But a simple fix might be:
```Julia
using JuMP, Ipopt
model = Model(Ipopt.Optimizer)
set_silent(model)
@variable(model, 0 <= L₁[i = 1:x, j = 1:y], start = start_values.mat1[i, j])
@variable(model, 0 <= L₂[i = 1:x, j = 1:y], start = start_values.mat2[i, j])
@objective(
model,
Min,
sum(
(detector_response(spectra.top[i,j, :], L₁[i,j], L₂[i,j]) - top_image[i, j])^2 +
(detector_response(spectra.bottom[i,j,:], L₁[i,j], L₂[i,j]) - bottom_image[i, j])^2
for i in 1:x, j in 1:y
)
)
optimize!(model)
```
See [Performance tips · JuMP](https://jump.dev/JuMP.jl/stable/tutorials/getting_started/performance_tips/#Use-macros-to-build-expressions)