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Finally, we call `value_iteration` to solve the specification. `value_iteration` returns the value function for all states in addition to the number of iterations performed and the last Bellman residual.
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Finally, we call [`solve`](@ref) to solve the specification. `solve` returns the value function for all states in addition to the number of iterations performed and the last Bellman residual, wrapped in a solution object.
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```julia
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V, k, residual =value_iteration(problem)
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sol =solve(problem) # or solve(problem, RobustValueIteration())
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V, k, res = sol
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# or alternatively
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V, k, res =value_function(sol), num_iterations(sol), residual(sol)
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```
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For now, only [`RobustValueIteration`](@ref) is supported, but more algorithms are planned.
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!!! note
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To use multi-threading for parallelization, you need to either start julia with `julia --threads <n|auto>` where `n` is a positive integer or to set the environment variable `JULIA_NUM_THREADS` to the number of threads you want to use. For more information, see [Multi-threading](https://docs.julialang.org/en/v1/manual/multi-threading/).
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