Mathematical optimization software
The GEKKO Python package solves large-scale mixed-integer and differential algebraic equations with nonlinear programming solvers (IPOPT, APOPT, BPOPT, SNOPT, MINOS). Modes of operation include machine learning, data reconciliation, real-time optimization, dynamic simulation, and nonlinear model predictive control. In addition, the package solves Linear programming (LP), Quadratic programming (QP), Quadratically constrained quadratic program (QCQP), Nonlinear programming (NLP), Mixed integer programming (MIP), and Mixed integer linear programming (MILP). GEKKO is available in Python and installed with pip from PyPI of the Python Software Foundation. pip install gekko GEKKO works on all platforms and with Python 2.7 and 3+. By default, the problem is sent to a public server where the solution is computed and returned to Python. There are Windows, MacOS, Linux, and ARM (Raspberry Pi) processor options to solve without an Internet connection. GEKKO is an extension of the APMonitor Optimization Suite but has integrated the modeling and solution visualization directly within Python. A mathematical model is expressed in terms of variables and equations such as the Hock & Schittkowski Benchmark Problem #71 used to test the performance of nonlinear programming solvers. This particular optimization problem has an objective function and subject to the inequality constraint and equality constraint . The four variables must be between a lower bound of 1 and an upper bound of 5. The initial guess values are . This optimization problem is solved with GEKKO as shown below. from gekko import GEKKOm = GEKKO # Initialize gekko# Initialize variablesx1 = m.Var(value=1, lb=1, ub=5)x2 = m.Var(value=5, lb=1, ub=5)x3 = m.Var(value=5, lb=1, ub=5)x4 = m.Var(value=1, lb=1, ub=5)# Equationsm.Equation(x1 * x2 * x3 * x4 >= 25)m.Equation(x1 ** 2 + x2 ** 2 + x3 ** 2 + x4 ** 2 == 40)m.Obj(x1 * x4 * (x1 + x2 + x3) + x3) # Objectivem.solve(disp=False) # Solveprint("x1: " + str(x1.value))print("x2: " + str(x2.value))print("x3: " + str(x3.value))print("x4: " + str(x4.value))print("Objective: " + str(m.options.objfcnval)) (Wikipedia).
A very basic overview of optimization, why it's important, the role of modeling, and the basic anatomy of an optimization project.
From playlist Optimization
Solving Optimization Problems with MATLAB | Master Class with Loren Shure
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From playlist Convex Optimization
[Calculus] Optimization 2 || Lecture 35
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From playlist Calculus 1
[Calculus] Optimization 1 || Lecture 34
Visit my website: http://bit.ly/1zBPlvm Subscribe on YouTube: http://bit.ly/1vWiRxW Hello, welcome to TheTrevTutor. I'm here to help you learn your college courses in an easy, efficient manner. If you like what you see, feel free to subscribe and follow me for updates. If you have any que
From playlist Calculus 1
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From playlist Distro Reviews
Constrained Optimization: Linear Programs
In this video we introduce the concept of linear optimization problems, AKA linear programs (LPs). LPs are optimization problems where the cost function and constraints are linear (or affine). We examine several examples of linear programs, discuss how to transform general LPs into stand
From playlist Optimization
21. 4 ways to solve systems of nonlinear equations in python
Non-linear equations are much nastier to solve than linear equations. Fortunately, we have lots of options in python. This video shows 4 approaches: graphical using matplotlib, numerically using scipy's fsolve optimizer, numerically using the gekko optimizer, and symbolically using sympy.
From playlist Intro to Python Programming for Materials Engineers
What in the world is a linear program?
What is a linear program and why do we care? Today I’m going to introduce you to the exciting world of optimization, which is the mathematical field of maximizing or minimizing an objective function subject to constraints. The most fundamental topic in optimization is linear programming,
From playlist Summer of Math Exposition 2 videos
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From playlist Fw:Thinking
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From playlist Be Smart - LATEST EPISODES!
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From playlist Sparsity and Compression [Data-Driven Science and Engineering]
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From playlist Optimization tutorial
Mathematica Experts Live: Numerical Optimization in Mathematica
Mathematica has a full range of state-of-the-art numerical optimization techniques. James Adduci gives an overview of the functionality in this presentation from Mathematica Experts Live: Numeric Modeling in Mathematica. For more information about Mathematica, please visit: http://www.wol
From playlist Mathematica Experts Live: Numeric Modeling in Mathematica
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From playlist REPTILES
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From playlist Long Term Reviews
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