CHORES: pre-commit

README.md

@@ -122,14 +122,15 @@ awaits you in this primer:
 
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+
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-## Installation
-
 <a name="01-installation"></a>
 
+## Installation
+
 We are using Python 3 in this primer and assume that you have a working Python 3
 installation as well as the basic knowledge to use it. There are also interfaces
 for other languages, but Python 3 is, in my opinion, the most convenient one, as
@@ -190,14 +191,15 @@ For further guidance, consider the
 which are likely to be similar. Since we frequently use Gurobi in addition to
 CP-SAT, our hardware choices were also influenced by their recommendations.
 
+
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-## Example
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+## Example
+
 Before we dive into any internals, let us take a quick look at a simple
 application of CP-SAT. This example is so simple that you could solve it by
 hand, but know that CP-SAT would (probably) be fine with you adding a thousand
@@ -390,16 +392,15 @@ very good at that).
 
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+<a name="section-alternatives"></a> <a name="03-big-picture"></a>
 
 ## Alternatives: CP-SAT's Place in the World of Optimization
 
-<a name="03-big-picture"></a>
-
 When you begin exploring optimization, you will encounter a plethora of tools,
 techniques, and communities. It can be overwhelming, as these groups, while
 sharing many similarities, also differ significantly. They might use the same
@@ -563,14 +564,15 @@ open-source tool.
 
 ---
 
+
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-## Modelling
-
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+## Modelling
+
 CP-SAT provides us with much more modelling options than the classical
 MIP-solver. Instead of just the classical linear constraints (<=, ==, >=), we
 have various advanced constraints such as `AllDifferent` or
@@ -1714,14 +1716,15 @@ If you need more, you can check out the
 
 ---
 
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-## Parameters
-
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+## Parameters
+
 The CP-SAT solver has a lot of parameters to control its behavior. They are
 implemented via
 [Protocol Buffer](https://developers.google.com/protocol-buffers) and can be
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 ---
 
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-## Coding Patterns for Optimization Problems
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+## Coding Patterns for Optimization Problems
+
 In this section, we will explore various coding patterns that are essential for
 structuring implementations for optimization problems using CP-SAT. While we
 will not delve into the modeling of specific problems, our focus will be on
@@ -2906,14 +2909,15 @@ class KnapsackSolver:
 
 ---
 
+
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-## How does it work?
-
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+## How does it work?
+
 CP-SAT is a versatile _portfolio_ solver, centered around a _Lazy Clause
 Generation (LCG)_ based Constraint Programming Solver, although it encompasses a
 broader spectrum of technologies.
@@ -3093,14 +3097,15 @@ At times, NP-hard problems inherently pose formidable challenges, leaving us
 with no alternative but to seek more manageable modeling approaches instead of
 looking for better solvers.
 
+
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-## Benchmarking your Model
-
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+## Benchmarking your Model
+
 Benchmarking is an essential step if your model is not yet meeting the
 performance standards of your application or if you are aiming for an academic
 publication. This process involves analyzing your model's performance,
@@ -3506,14 +3511,15 @@ and nice plots on which we can base our decisions.
 > [Algorithm Selection](https://en.wikipedia.org/wiki/Algorithm_selection)
 > problem and can be surprisingly complex, too.
 
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-## Using CP-SAT for Bigger Problems with Large Neighborhood Search
-
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+## Using CP-SAT for Bigger Problems with Large Neighborhood Search
+
 CP-SAT is great at solving small and medium-sized problems. But what if you have
 a really big problem on your hands? One option might be to use a special kind of
 algorithm known as a "meta-heuristic", like a
@@ -3790,3 +3796,5 @@ aware of the exploration vs. exploitation dilemma and that many smart people
 have already thought about it.
 
 > TODO: Continue...
+
+

build.py

@@ -77,7 +77,7 @@ def convert_for_mdbook(content):
             with open(file, "r") as current_file:
                 content = current_file.read()
                 f.write(disclaimer)
-                f.write(f"<!-- {file}" + " -->\n")
+                f.write(f"<!-- {file} -->\n")
                 f.write(content)
                 f.write("\n\n")
                 Path("./.mdbook/").mkdir(parents=True, exist_ok=True)