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| <img src="https://github.com/PSORLab/EAGO.jl/blob/master/docs/src/assets/logo.png" width="75%" height="75%"> | ||
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| # EAGO - Easy Advanced Global Optimization | ||
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| EAGO is an open-source development environment for **robust and global optimization** in Julia. See the full [README](https://github.com/PSORLab/EAGO.jl/blob/master/README.md) for more information. | ||
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| | **PSOR Lab** | **Current Version** | **Build Status** | **Documentation** | | ||
| |:------------:|:-------------------:|:----------------:|:-----------------:| | ||
| | [](https://psor.uconn.edu/) | [](https://juliahub.com/ui/Packages/General/EAGO) | [](https://github.com/PSORLab/EAGO.jl/actions?query=workflow%3ACI) [](https://codecov.io/gh/PSORLab/EAGO.jl)| [](https://PSORLab.github.io/EAGO.jl/dev) | | ||
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| EAGO is a deterministic global optimizer designed to address a wide variety of optimization problems, emphasizing nonlinear programs (NLPs), by propagating McCormick relaxations along the factorable structure of each expression in the NLP. Most operators supported by modern automatic differentiation (AD) packages are supported by EAGO and a number utilities for sanitizing native Julia code and generating relaxations on a wide variety of user-defined functions have been included. Currently, EAGO supports problems that have a priori variable bounds defined and have differentiable constraints. That is, problems should be specified in the generic form below: | ||
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| $$ | ||
| \begin{align*} | ||
| f^{\*} = & \min_{\mathbf y \in Y \subset \mathbb R^{n_{y}}} f(\mathbf y) \\ | ||
| {\rm s.t.} \\;\\; & \mathbf h(\mathbf y) = \mathbf 0 \\ | ||
| & \mathbf g(\mathbf y) \leq \mathbf 0 \\ | ||
| & Y = [\mathbf y^{\mathbf L}, \mathbf y^{\mathbf U}] \in \mathbb{IR}^{n} \\ | ||
| & \qquad \mathbf y^{\mathbf L}, \mathbf y^{\mathbf U} \in \mathbb R^{n} | ||
| \end{align*} | ||
| $$ | ||
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| For each nonlinear term, EAGO makes use of factorable representations to construct bounds and relaxations. In the case of $f(x) = x (x - 5) \sin(x)$, a list is generated and rules for constructing McCormick relaxations are used to formulate relaxations in the original decision space, $X$ [[1](#references)]: | ||
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| - $v_{1} = x$ | ||
| - $v_{2} = v_{1} - 5$ | ||
| - $v_{3} = \sin(v_{1})$ | ||
| - $v_{4} = v_{1} v_{2}$ | ||
| - $v_{5} = v_{4} v_{3}$ | ||
| - $f(x) = v_{5}$ | ||
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| <p align="center"> | ||
| <img src="https://github.com/PSORLab/EAGO.jl/blob/master/docs/src/mccormick/Figure_1.png" width="60%" height="60%"> | ||
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| Either these original relaxations, differentiable McCormick relaxations [[2](#references)], or affine relaxations thereof can be used to construct relaxations of optimization problems useful in branch and bound routines for global optimization. Utilities are included to combine these with algorithms for relaxing implicit functions [[3](#references)] and forward-reverse propagation of McCormick arithmetic [[4](#references)]. | ||
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| ## License | ||
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| EAGO is licensed under the [MIT License](https://github.com/PSORLab/EAGO.jl/blob/master/LICENSE.md). | ||
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| ## Installation | ||
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| EAGO is a registered Julia package and it can be installed using the Julia package manager: | ||
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| ```julia | ||
| import Pkg | ||
| Pkg.add("EAGO") | ||
| ``` | ||
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| ## Use with JuMP | ||
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| EAGO makes use of JuMP to improve the user's experience in setting up optimization models. Consider the "process" problem instance from [[5](#references)]: | ||
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| $$ | ||
| \begin{align*} | ||
| & \max_{\mathbf x \in X} 0.063 x_{4} x_{7} - 5.04 x_{1} - 0.035 x_{2} - 10 x_{3} - 3.36 x_{2} \\ | ||
| {\rm s.t.} \\;\\; & x_{1} (1.12 + 0.13167 x_{8} - 0.00667 x_{8}^{2}) + x_{4} = 0 \\ | ||
| & -0.001 x_{4} x_{9} x_{6} / (98 - x_{6}) + x_{3} = 0 \\ | ||
| & -(1.098 x_{8} - 0.038 x_{8}^{2}) - 0.325 x_{6} + x_{7} = 0 \\ | ||
| & -(x_{2} + x_{5}) / x_{1} + x_{8} = 0 \\ | ||
| & -x_{1} + 1.22 x_{4} - x_{5} = 0 \\ | ||
| & x_{9} + 0.222 x_{10} - 35.82 = 0 \\ | ||
| & -3.0 x_{7} + x_{10} + 133.0 = 0 \\ | ||
| & X = [10, 2000] \times [0, 16000] \times [0, 120] \times [0, 5000] \\ | ||
| & \qquad \times [0, 2000] \times [85, 93] \times [90,9 5] \times [3, 12] \times [1.2, 4] \times [145, 162] | ||
| \end{align*} | ||
| $$ | ||
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| This model can be formulated in Julia as: | ||
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| ```julia | ||
| using JuMP, EAGO | ||
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| # Build model using EAGO's optimizer | ||
| m = Model(EAGO.Optimizer) | ||
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| # Define bounded variables | ||
| xL = [10.0; 0.0; 0.0; 0.0; 0.0; 85.0; 90.0; 3.0; 1.2; 145.0] | ||
| xU = [2000.0; 16000.0; 120.0; 5000.0; 2000.0; 93.0; 95.0; 12.0; 4.0; 162.0] | ||
| @variable(m, xL[i] <= x[i=1:10] <= xU[i]) | ||
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| # Define nonlinear constraints | ||
| @NLconstraint(m, e1, -x[1]*(1.12 + 0.13167*x[8] - 0.00667*(x[8])^2) + x[4] == 0.0) | ||
| @NLconstraint(m, e3, -0.001*x[4]*x[9]*x[6]/(98.0 - x[6]) + x[3] == 0.0) | ||
| @NLconstraint(m, e4, -(1.098*x[8] - 0.038*(x[8])^2) - 0.325*x[6] + x[7] == 57.425) | ||
| @NLconstraint(m, e5, -(x[2] + x[5])/x[1] + x[8] == 0.0) | ||
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| # Define linear constraints | ||
| @constraint(m, e2, -x[1] + 1.22*x[4] - x[5] == 0.0) | ||
| @constraint(m, e6, x[9] + 0.222*x[10] == 35.82) | ||
| @constraint(m, e7, -3.0*x[7] + x[10] == -133.0) | ||
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| # Define nonlinear objective | ||
| @NLobjective(m, Max, 0.063*x[4]*x[7] - 5.04*x[1] - 0.035*x[2] - 10*x[3] - 3.36*x[5]) | ||
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| # Solve the optimization problem | ||
| JuMP.optimize!(m) | ||
| ``` | ||
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| ## Documentation | ||
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| EAGO has numerous features: a solver accessible from JuMP/MathOptInterface (MOI), domain reduction routines, McCormick relaxations, and specialized nonconvex semi-infinite program solvers. A full description of all features can be found on the [documentation website](https://psorlab.github.io/EAGO.jl/dev/). A series of example have been provided in the documentation and in the form of Jupyter Notebooks in the separate [EAGO-notebooks](https://github.com/PSORLab/EAGO-notebooks) repository. | ||
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| ## A Cautionary Note on Global Optimization | ||
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| As a global optimization platform, EAGO's solvers can be used to find solutions of general nonconvex problems with a guaranteed certificate of optimality. However, global solvers suffer from the curse of dimensionality and therefore their performance is outstripped by convex/local solvers. For users interested in large-scale applications, be warned that problems generally larger than a few variables may prove challenging for certain types of global optimization problems. | ||
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| ## Citing EAGO | ||
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| Please cite the following paper when using EAGO. In plain text form this is: | ||
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| ``` | ||
| Wilhelm, M.E. and Stuber, M.D. EAGO.jl: easy advanced global optimization in Julia. | ||
| Optimization Methods and Software. 37(2): 425-450 (2022). DOI: 10.1080/10556788.2020.1786566 | ||
| ``` | ||
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| As a BibTeX entry: | ||
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| ```bibtex | ||
| @article{doi:10.1080/10556788.2020.1786566, | ||
| author = {Wilhelm, M.E. and Stuber, M.D.}, | ||
| title = {EAGO.jl: easy advanced global optimization in Julia}, | ||
| journal = {Optimization Methods and Software}, | ||
| volume = {37}, | ||
| number = {2}, | ||
| pages = {425-450}, | ||
| year = {2022}, | ||
| publisher = {Taylor & Francis}, | ||
| doi = {10.1080/10556788.2020.1786566}, | ||
| URL = {https://doi.org/10.1080/10556788.2020.1786566}, | ||
| eprint = {https://doi.org/10.1080/10556788.2020.1786566} | ||
| } | ||
| ``` | ||
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| ## References | ||
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| 1. Mitsos, A., Chachuat, B., and Barton, P.I. **McCormick-based relaxations of algorithms.** *SIAM Journal on Optimization*. 20(2): 573–601 (2009). | ||
| 2. Khan, K.A., Watson, H.A.J., and Barton, P.I. **Differentiable McCormick relaxations.** *Journal of Global Optimization*. 67(4): 687-729 (2017). | ||
| 3. Stuber, M.D., Scott, J.K., and Barton, P.I.: **Convex and concave relaxations of implicit functions.** *Optimization Methods and Software* 30(3): 424–460 (2015). | ||
| 4. Wechsung, A., Scott, J.K., Watson, H.A.J., and Barton, P.I. **Reverse propagation of McCormick relaxations.** *Journal of Global Optimization* 63(1): 1-36 (2015). | ||
| 5. Bracken, J., and McCormick, G.P. *Selected Applications of Nonlinear Programming.* John Wiley and Sons, New York (1968). |