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  3. Introduction to Optimization in Python
  • 1

    Introduction to Optimization

    Gratuito

    This chapter introduces optimization, its core components, and its wide applications across industries and domains. It presents a quick, exhaustive search method for solving an optimization problem. It provides a mathematical primer for the concepts required for this course.

  • 2

    Unconstrained and Linear Constrained Optimization

    This chapter covers solving unconstrained and constrained optimization problems with differential calculus and SymPy, identifying potential pitfalls. SciPy is also introduced to solve unconstrained optimization problems, in single and multiple dimensions, numerically, with a few lines of code. The chapter goes on to solve linear programming in SciPy and PuLP.

  • 3

    Non-linear Constrained Optimization

    Gratuito

    This chapter introduces convex-constrained optimization problems with different constraints and looks at mixed integer linear programming problems, essentially linear programming problems where at least one variable is an integer.

  • 4

    Robust Optimization Techniques

    This chapter covers finding the global optimum when multiple good solutions exist. We will conduct sensitivity analysis and learn linearization techniques that reduce non-linear problems to easily solvable ones with SciPy or PuLP. In terms of applications, we will solve an HR allocation with training costs problem and capital budgeting with dependent projects.

Initializing

Exercicio

The diet problem revisited

You are reviewing the financials for a farmer who asked you to revisit the diet of his pigs and cut on the cost if possible. The current cost minimization diet is based on the vet's recommendation for at least 17% protein, 2% fat, 7lb food following specifications

Food Cost ($/lb) Protein (%) Fat (%)
corn 0.11 10 2.5
soybean 0.28 40 1

You have the information that the 7 lb was a rounded figure and could decrease to 6.6 lb. You are asked to see how changing the weight or fat constraints one at a time affects the minimum cost. You will solve the original problem as is and examine the slack and shadow price.

pulp has been imported for you and model has been defined as well as the variables C and S for corn and soybean.

Instruções

100 XP
  • Print the slack of the Weight constraint.
  • Check if the shadow price of the Weight constraint is greater than 0.