Fix broken links and numbering

mobook · Nov 22, 2023 · 40a7e30 · 40a7e30
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diff --git a/notebooks/05/04-svm-binary-classification.ipynb b/notebooks/05/04-svm-binary-classification.ipynb
@@ -15,7 +15,7 @@
     "```\n",
     "```{index} single: application; counterfeit banknotes\n",
     "```\n",
-    "# 5.4 Support Vector Machines for Binary Classification\n",
+    "# 5.4 Support Vector Machines for binary classification\n",
     "\n",
     "Support Vector Machines (SVM) are a type of supervised machine learning model. Similar to other machine learning techniques based on regression, training an SVM classifier uses examples with known outcomes, and involves optimization some measure of performance. The resulting classifier can then be applied to classify data with unknown outcomes.\n",
     "\n",

diff --git a/notebooks/05/05.00.md b/notebooks/05/05.00.md
@@ -10,7 +10,7 @@ This chapter includes several examples with companion Pyomo implementation that
 * [Ordinary Least Squares (OLS) regression](02-ols-regression.ipynb)
 * [Markowitz portfolio optimization problem](03-markowitz-portfolio.ipynb)
 * [Support Vector Machines for binary classification](04-svm-binary-classification.ipynb)
-* [Extra material: Refinery production](05-refinery-production.ipynb)
+* [Extra material: Refinery production and shadow pricing with CVXPY](05-refinery-production.ipynb)
 * [Extra material: Cutting stock](06-cutting-stock.ipynb)
 
 Go to the [next chapter](../06/06.00.md) about conic optimization.
diff --git a/notebooks/06/01-economic-order-quantity.ipynb b/notebooks/06/01-economic-order-quantity.ipynb
@@ -18,7 +18,7 @@
     "```{index} single: application; inventory management\n",
     "```\n",
     "\n",
-    "# Economic Order Quantity\n",
+    "# 6.1 Economic Order Quantity\n",
     "\n",
     "This notebooks demonstrates the reformulation of hyperbolic constraints as SOCO with implementation with `pyomo.kernel.conic.quadratic` and also the direct modeling of the hyperbolic constraint with `pyomo.kernel.conic.rotated_quadratic`. The example is familiar to any MBA/business student, and has a significant range of applications including warehouse operations.\n",
     "\n",

diff --git a/notebooks/06/02-kelly-criterion.ipynb b/notebooks/06/02-kelly-criterion.ipynb
@@ -20,9 +20,9 @@
     "```{index} single: application; investment\n",
     "```\n",
     "\n",
-    "# The Kelly Criterion\n",
+    "# 6.2 The Kelly criterion\n",
     "\n",
-    "The Kelly Criterion determines the size of a bet in a repeated game with binary outcomes. The analysis was proposed in 1956 by John Kelly at Bell Laboratories. Kelly identified an analogy between gambling on binary outcomes and Claude Shannon's work on encoding information for transmission on noisy channels. Kelly used the analogy to show\n",
+    "The Kelly criterion determines the size of a bet in a repeated game with binary outcomes. The analysis was proposed in 1956 by John Kelly at Bell Laboratories. Kelly identified an analogy between gambling on binary outcomes and Claude Shannon's work on encoding information for transmission on noisy channels. Kelly used the analogy to show\n",
     "\n",
     "> The maximum exponential rate of growth of the gambler's capital is equal to the rate of transmission of information over the channel.\n",
     "\n",

diff --git a/notebooks/06/03-markowitz-portfolio-revisited.ipynb b/notebooks/06/03-markowitz-portfolio-revisited.ipynb
@@ -14,7 +14,7 @@
     "```{index} single: solver; cplex\n",
     "```\n",
     "\n",
-    "# Markowitz portfolio optimization revisited"
+    "# 6.3 Markowitz portfolio optimization revisited"
    ]
   },
   {

diff --git a/notebooks/06/04-building-insulation.ipynb b/notebooks/06/04-building-insulation.ipynb
@@ -14,7 +14,7 @@
     "```\n",
     "```{index} single: application; building insulation\n",
     "```\n",
-    "# Optimal Design of Multilayered Building Insulation"
+    "# 6.4 Optimal Design of Multilayered Building Insulation"
    ]
   },
   {

diff --git a/notebooks/06/05-svm-conic.ipynb b/notebooks/06/05-svm-conic.ipynb
@@ -19,15 +19,15 @@
     "```\n",
     "```{index} single: application; counterfeit banknotes\n",
     "```\n",
-    "# Support Vector Machines with Conic Optimization\n"
+    "# 6.5 Support Vector Machines with Conic Optimization\n"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "8486fa79",
    "metadata": {},
    "source": [
-    "In this notebook we come back to the concept of training support vector machines as we did in [the first support vector machine notebook](../05/svm.ipynb)\n",
+    "In this notebook we come back to the concept of training support vector machines as we did in [the first support vector machine notebook](../05/04-svm-binary-classification.ipynb)\n",
     "\n",
     "The difference is that we shall now be solving the dual problems related to training the SVM's using the conic quadratic optimization by explicitly calling the Mosek solver, which should yield more stable numerical results than general convex optimization.\n",
     "\n",

diff --git a/notebooks/07/01-fleet-assignment.ipynb b/notebooks/07/01-fleet-assignment.ipynb
@@ -26,7 +26,7 @@
     "```{index} Gantt charts\n",
     "```\n",
     "\n",
-    "# Fleet assignment problem"
+    "# 7.1 Fleet assignment problem"
    ]
   },
   {

diff --git a/notebooks/07/02-bim-robustness-analysis.ipynb b/notebooks/07/02-bim-robustness-analysis.ipynb
@@ -24,7 +24,7 @@
     "```{index} simulations\n",
     "```\n",
     "\n",
-    "# Robustness analysis of BIM production plan via simulations"
+    "# 7.2 Robustness analysis of BIM production plan via simulations"
    ]
   },
   {

diff --git a/notebooks/08/01-bim-robust-optimization.ipynb b/notebooks/08/01-bim-robust-optimization.ipynb
@@ -6,7 +6,7 @@
     "id": "TP7ReUS8K3iE"
    },
    "source": [
-    "# Robust BIM microchip production problem"
+    "# 8.1 Robust BIM microchip production problem"
    ]
   },
   {

diff --git a/notebooks/09/01-pop-up-shop.ipynb b/notebooks/09/01-pop-up-shop.ipynb
@@ -15,7 +15,7 @@
     "```{index} stochastic optimization\n",
     "```\n",
     "\n",
-    "# Pop-up shop"
+    "# 9.1 Pop-up shop"
    ]
   },
   {

diff --git a/notebooks/09/02-markowitz-portfolio-with-chance-constraint.ipynb b/notebooks/09/02-markowitz-portfolio-with-chance-constraint.ipynb
@@ -15,7 +15,7 @@
     "```\n",
     "```{index} chance constraints\n",
     "```\n",
-    "# Markowitz portfolio optimization with chance constraints"
+    "# 9.2 Markowitz portfolio optimization with chance constraints"
    ]
   },
   {

diff --git a/notebooks/09/03-seafood-distribution-center.ipynb b/notebooks/09/03-seafood-distribution-center.ipynb
@@ -17,7 +17,7 @@
     "```{index} simulations\n",
     "```\n",
     "\n",
-    "# Stock optimization for seafood distribution center"
+    "# 9.3 Stock optimization for seafood distribution center"
    ]
   },
   {

diff --git a/notebooks/09/04-economic-dispatch.ipynb b/notebooks/09/04-economic-dispatch.ipynb
@@ -17,7 +17,7 @@
     "```{index} mutable parameters\n",
     "```\n",
     "\n",
-    "# Economic dispatch in energy systems"
+    "# 9.4 Economic dispatch in energy systems"
    ]
   },
   {

diff --git a/notebooks/10/01-airline-seating.ipynb → ...ks/10/01-airline-seating-allocation.ipynb b/notebooks/10/01-airline-seating.ipynb → ...ks/10/01-airline-seating-allocation.ipynb
@@ -24,7 +24,7 @@
     "```{index} two-stage problem\n",
     "```\n",
     "\n",
-    "# Airline seat allocation problem\n",
+    "# 10.1 Airline seat allocation problem\n",
     "\n",
     "## Attribution\n",
     "\n",

diff --git a/notebooks/10/02-opf-linear-decision-rule.ipynb b/notebooks/10/02-opf-linear-decision-rule.ipynb
@@ -20,7 +20,7 @@
     "```\n",
     "```{index} linear decision rules\n",
     "```\n",
-    "# Optimal power flow problem with recourse actions\n",
+    "# 10.2 Optimal power flow problem with recourse actions\n",
     "\n",
     "In this notebook we illustrate an application of the idea of linear decision rules to a two-stage optimal power flow problem in which the power of the conventional generators has to adapt automatically to balance out energy surplus/shortage due to changes in renewable resources input.\n",
     "\n",

diff --git a/notebooks/10/03-two-stage-production-planning.ipynb b/notebooks/10/03-two-stage-production-planning.ipynb
@@ -21,7 +21,7 @@
     "```{index} constraint and column generation\n",
     "```\n",
     "\n",
-    "# Two-stage production planning using constraint and column generation\n",
+    "# 10.3 Two-stage production planning using constraint and column generation\n",
     "\n",
     "The purpose of this notebook is to demonstrate a range of techniques for two-stage optimization (robust and stochastic) using a range of techniques:\n",
     "* Robust Optimization (including Constraint and Column Generation)\n",

diff --git a/notebooks/10/10.00.md b/notebooks/10/10.00.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 two-stage problems affected by uncertainty:
 
-* [Airline seating splitting](01-airline-seating.ipynb)
+* [Airline seating splitting](01-airline-seating-allocation.ipynb)
 * [Optimal power flow problem with recourse actions](02-opf-linear-decision-rule.ipynb), a two-stage variant of the problem from [Chapter 4](../04/04.00.md) that uses linear decision rules.
-* [Two-stage production planning](03-column-constraint-generation.ipynb), a multi-stage variant of the introductory problem from [Chapter 1](../01/01.00.md) solved both with SAA and CCG.
+* [Two-stage production planning](03-two-stage-production-planning.ipynb), a multi-stage variant of the introductory problem from [Chapter 1](../01/01.00.md) solved both with SAA and CCG.
 * [Extra: Farmer land allocation](04-farmer-problem.ipynb)
 * [Extra: Two-stage energy dispatch optimization with wind curtailment](05-opf-wind-curtailment.ipynb), a two-stage variant of the problem from [Chapter 4](../04/04.00.md) with binary second-stage variables.