Change the order in which a few notebooks appear

_toc.yml

@@ -14,9 +14,9 @@ chapters:
   - file: notebooks/02/01-bim.ipynb
   - file: notebooks/02/02-lad-regression.ipynb
   - file: notebooks/02/03-mad-portfolio-optimization.ipynb
+  - file: notebooks/02/04-bim-maxmin.ipynb
+  - file: notebooks/02/05-bim-fractional.ipynb
   - file: notebooks/02/04-bim-dual.ipynb
-  - file: notebooks/02/05-bim-maxmin.ipynb
-  - file: notebooks/02/06-bim-fractional.ipynb
   - file: notebooks/02/07-bim-demand-forecast.ipynb
   - file: notebooks/02/08-L1-regression-wine-quality.ipynb
   - file: notebooks/02/09-production-faciliity-worst-case.ipynb
@@ -75,8 +75,8 @@ chapters:
 
 - file: notebooks/09/09.00.md
   sections:
-  - file: notebooks/09/01-pop-up-shop.ipynb
-  - file: notebooks/09/02-markowitz-portfolio-with-chance-constraint.ipynb
+  - file: notebooks/09/01-markowitz-portfolio-with-chance-constraint.ipynb
+  - file: notebooks/09/02-pop-up-shop.ipynb
   - file: notebooks/09/03-seafood-distribution-center.ipynb
   - file: notebooks/09/04-economic-dispatch.ipynb
 

notebooks/02/02.00.md

@@ -21,9 +21,9 @@ This chapter includes several with companion Pyomo implementation that explore v
 * [Microchip production problem of company BIM](01-bim.ipynb)
 * [Least Absolute Deviation (LAD) Regression](02-lad-regression.ipynb)
 * [Mean Absolute Deviation (MAD) portfolio optimization](03-mad-portfolio-optimization.ipynb)
-* [The dual problem of the microchip production problem](04-bim-dual.ipynb)
-* [A variant of BIM problem: maximizing the lowest possible profit](05-bim-maxmin.ipynb)
-* [Two variants of the BIM problem using fractional objective or additional fixed costs](06-bim-fractional.ipynb)
+* [A variant of BIM problem: maximizing the lowest possible profit](04-bim-maxmin.ipynb)
+* [Two variants of the BIM problem using fractional objective or additional fixed costs](05-bim-fractional.ipynb)
+* [The dual problem of the microchip production problem](06-bim-dual.ipynb)
 * [The BIM production problem using demand forecasts](07-bim-demand-forecast.ipynb)
 * [Extra material: Wine quality prediction problem using $L_1$ regression](08-L1-regression-wine-quality.ipynb)
 * [Extra material: Multi-product facility production](09-production-faciliity-worst-case.ipynb)

notebooks/02/05-bim-maxmin.ipynb → notebooks/02/04-bim-maxmin.ipynb

@@ -11,7 +11,7 @@
     "```{index} minmax objective\n",
     "```\n",
     "\n",
-    "# 2.5 BIM production for worst case"
+    "# 2.4 BIM production for worst case"
    ]
   },
   {

notebooks/02/06-bim-fractional.ipynb → notebooks/02/05-bim-fractional.ipynb

@@ -11,7 +11,7 @@
     "```{index} single: Pyomo; Expression\n",
     "```\n",
     "\n",
-    "# 2.6 BIM production variants"
+    "# 2.5 BIM production variants"
    ]
   },
   {

notebooks/02/04-bim-dual.ipynb → notebooks/02/06-bim-dual.ipynb

@@ -16,7 +16,7 @@
     "```{index} single: Pyomo; Suffix\n",
     "```\n",
     "\n",
-    "# 2.4 Dual of the BIM production problem"
+    "# 2.6 Dual of the BIM production problem"
    ]
   },
   {

notebooks/09/02-markowitz-portfolio-with-chance-constraint.ipynb → notebooks/09/01-markowitz-portfolio-with-chance-constraint.ipynb

@@ -15,7 +15,7 @@
     "```\n",
     "```{index} chance constraints\n",
     "```\n",
-    "# 9.2 Markowitz portfolio optimization with chance constraints"
+    "# 9.1 Markowitz portfolio optimization with chance constraints"
    ]
   },
   {
@@ -75,11 +75,11 @@
    },
    "source": [
     "## Problem description\n",
-    "We consider here another variant of the Markowitz portfolio optimization problem, which we already encountered in the context of convex optimization [here](../05/markowitz_portfolio.ipynb) and in the context of conic optimization [here](../06/markowitz_portfolio_revisited.ipynb).\n",
+    "We consider here another variant of the Markowitz portfolio optimization problem, which we already encountered in the context of convex optimization [here](../05/03-markowitz_portfolio.ipynb) and in the context of conic optimization [here](../06/03-markowitz_portfolio_revisited.ipynb).\n",
     "\n",
     "Assuming there is an initial unit capital $C$ that needs to be invested in a selection of $n$ possible assets, each of them with a unknown return rate $r_i$, $i=1,\\dots,n$. Let $x$ be the vector whose $i$-th component $x_i$ describes the fraction of the capital invested in asset $i$. The return rate vector $r$ can be modelled by a multivariate Gaussian distribution with mean $\\mu$ and covariance $\\Sigma$. Assume there is also a risk-free asset with guaranteed return rate $R$ and let $\\tilde{x}$ the amount invested in that asset. We want to determine the portfolio that maximizes the _expected_ return $\\mathbb{E} ( R \\tilde{x} + r^\\top x )$, which in view of our assumptions rewrites as $ \\mathbb{E} ( R \\tilde{x} + r^\\top x ) = R \\tilde{x} + \\mu^\\top x$.\n",
     "\n",
-    "Additionally, we includ a _loss risk chance constraint_ of the form \n",
+    "Additionally, we include a _loss risk chance constraint_ of the form \n",
     "\n",
     "$$\n",
     "\\mathbb{P} ( r^\\top x \\leq \\alpha) \\leq \\beta.\n",

notebooks/09/01-pop-up-shop.ipynb → notebooks/09/02-pop-up-shop.ipynb

@@ -15,7 +15,7 @@
     "```{index} stochastic optimization\n",
     "```\n",
     "\n",
-    "# 9.1 Pop-up shop"
+    "# 9.2 Pop-up shop"
    ]
   },
   {

notebooks/09/09.00.md

@@ -2,8 +2,8 @@
 
 In this chapter, there is a number of examples with companion Pyomo implementation that explore various modeling and implementation aspects of stochastic optimization:
 
-* [Optimal management of a pop-up shop](01-pop-up-shop.ipynb)
-* [Markowitz portfolio with chance constraints](02-markowitz-portfolio-with-chance-constraint.ipynb)
+* [Markowitz portfolio with chance constraints](01-markowitz-portfolio-with-chance-constraint.ipynb)
+* [Optimal management of a pop-up shop](02-pop-up-shop.ipynb)
 * [Stock optimization for seafood distribution center](03-seafood-distribution-center.ipynb)
 * [Economic dispatch in energy systems using chance constraints](04-economic-dispatch.ipynb)