# DynamicNLPModels.jl

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DynamicNLPModels.jl is a package for Julia designed for representing linear model predictive control (MPC) problems. It includes an API for building a model from user defined data and querying solutions.

## Installation

To install this package, please use

```
using Pkg
Pkg.add(url="https://github.com/MadNLP/DynamicNLPModels.jl.git")
```

or

`pkg> add https://github.com/MadNLP/DynamicNLPModels.jl.git`

## Overview

DynamicNLPModels.jl can construct both sparse and condensed formulations for MPC problems based on user defined data. We use the methods discussed by Jerez et al. to eliminate the states and condense the problem. DynamicNLPModels.jl constructs models that are subtypes of `AbstractNLPModel`

from NLPModels.jl enabling both the sparse and condensed models to be solved with a variety of different solver packages in Julia. DynamicNLPModels was designed in part with the goal of solving linear MPC problems on the GPU. This can be done within MadNLP.jl using MadNLPGPU.jl.

The general sparse formulation used within DynamicNLPModels.jl is

where

The condensed formulation used within DynamicNLPModels.jl is

## Getting Started

DynamicNLPModels.jl takes user defined data to form a `SparseLQDyanmicModel`

or a `DenseLQDynamicModel`

. The user can first create an object containing the `LQDynamicData`

, or they can pass the data directly to the `SparseLQDynamicModel`

or `DenseLQDynamicModel`

constructors.

```
using DynamicNLPModels, Random, LinearAlgebra
Q = 1.5 * Matrix(I, (3, 3))
R = 2.0 * Matrix(I, (2, 2))
A = rand(3, 3)
B = rand(3, 2)
N = 5
s0 = [1.0, 2.0, 3.0]
lqdd = LQDynamicData(s0, A, B, Q, R, N; **kwargs)
sparse_lqdm = SparseLQDynamicModel(lqdd)
dense_lqdm = DenseLQDynamicModel(lqdd)
# or
sparse_lqdm = SparseLQDynamicModel(s0, A, B, Q, R, N; **kwargs)
dense_lqdm = DenseLQDynamicModel(s0, A, B, Q, R, N; **kwargs)
```

Optional data (such as `sparse_lqdm`

or `dense_lqdm`

can be solved by different solvers such as MadNLP.jl or Ipopt (Ipopt requires the extension NLPModelsIpopt.jl). An example script under `\examples`

shows how the dense problem can be solved on a GPU using MadNLPGPU.jl.

DynamicNLPModels.jl also includes an API for querying solutions and reseting data. Solutions can be queried using `get_u(solver_ref, dynamic_model)`

and `get_s(solver_ref, dynamic_model)`

. The problem can be reset with a new `reset_s0!(dynamic_model, s0)`

.