From ebff8c536160341f836f68dfcfb2dc953f3784e1 Mon Sep 17 00:00:00 2001 From: Dimitri Lozeve Date: Wed, 8 Apr 2020 17:40:56 +0200 Subject: [PATCH] Add post on OR --- _site/archive.html | 4 + _site/atom.xml | 46 ++++++++++ _site/index.html | 4 + .../posts/operations-research-references.html | 92 +++++++++++++++++++ _site/rss.xml | 46 ++++++++++ posts/operations-research-references.org | 44 +++++++++ 6 files changed, 236 insertions(+) create mode 100644 _site/posts/operations-research-references.html create mode 100644 posts/operations-research-references.org diff --git a/_site/archive.html b/_site/archive.html index 370c561..c9b5f6b 100644 --- a/_site/archive.html +++ b/_site/archive.html @@ -51,6 +51,10 @@ ICLR 2020 Notes: Speakers and Workshops - May 5, 2020 +
  • + Operations Research and Optimisation: where to start? - April 8, 2020 +
    • +
  • Reading notes: Hierarchical Optimal Transport for Document Representation - April 5, 2020
    • diff --git a/_site/atom.xml b/_site/atom.xml index f50f860..ef6e778 100644 --- a/_site/atom.xml +++ b/_site/atom.xml @@ -65,6 +65,52 @@ ]]> + + Operations Research and Optimisation: where to start? + + https://www.lozeve.com/posts/operations-research-references.html + 2020-04-08T00:00:00Z + 2020-04-08T00:00:00Z + +
    + +
    +
    +

    Operations research (OR) is a vast area comprising a lot of theory, different branches of mathematics, and too many applications to count. In this post, I will try to explain why I find it so fascinating, but also why it can be a little disconcerting to explore at first. Then I will try to ease the newcomer’s path in this rich area, by suggesting a very rough “map” of the field and a few references to get started.

    +

    Keep in mind that although I studied it during my graduate studies, this is not my primary area of expertise (I’m a data scientist by trade), and I definitely don’t pretend to know everything in OR. This is a field too vast for any single person to understand in its entirety, and I talk mostly from a “amateur mathematician and computer scientist” standpoint.

    +

    Why is it hard to approach?

    +
      +
    • why it may be more difficult to approach than other, more recent areas like ML and DL +
        +
      • slightly longer history
        • +
      • always very close to applications: somehow more “messy” in its notations, vocabulary, standard references, etc, as other “purer” fields of maths (similar to stats in this regard)
        • +
      • often approached from a applied point of view means that many very different concepts are often mixed together
        • +
      • +
    • why it is interesting and you should pursue it anyway +
        +
      • history of the field
        • +
      • examples of applications
        • +
      • theory perspective, rigorous field
        • +
      • +
    • different subfields +
        +
      • optimisation: constrained and unconstrained
        • +
      • game theory
        • +
      • dynamic programming
        • +
      • stochastic processes
        • +
      • simulation
        • +
      • +
    • how to learn and practice +
        +
      • references
        • +
      • courses
        • +
      • computational assets
        • +
      • +
    +
    + +]]>     +     Reading notes: Hierarchical Optimal Transport for Document Representation diff --git a/_site/index.html b/_site/index.html index 299d0eb..2a7c926 100644 --- a/_site/index.html +++ b/_site/index.html @@ -74,6 +74,10 @@ public key: RWQ6uexORp8f7USHA7nX9lFfltaCA9x6aBV06MvgiGjUt6BVf6McyD26 ICLR 2020 Notes: Speakers and Workshops - May 5, 2020 +
  • + Operations Research and Optimisation: where to start? - April 8, 2020 +
    • +
  • Reading notes: Hierarchical Optimal Transport for Document Representation - April 5, 2020
    • diff --git a/_site/posts/operations-research-references.html b/_site/posts/operations-research-references.html new file mode 100644 index 0000000..c522320 --- /dev/null +++ b/_site/posts/operations-research-references.html @@ -0,0 +1,92 @@ + + + + + + + + + Dimitri Lozeve - Operations Research and Optimisation: where to start? + + + + + + + + + + + + + + + + +
    + +
    + + +

    Operations Research and Optimisation: where to start?

    + + +

    April 8, 2020

    + +
    + + + +
    + +
    +
    + +
    +
    +

    Operations research (OR) is a vast area comprising a lot of theory, different branches of mathematics, and too many applications to count. In this post, I will try to explain why I find it so fascinating, but also why it can be a little disconcerting to explore at first. Then I will try to ease the newcomer’s path in this rich area, by suggesting a very rough “map” of the field and a few references to get started.

    +

    Keep in mind that although I studied it during my graduate studies, this is not my primary area of expertise (I’m a data scientist by trade), and I definitely don’t pretend to know everything in OR. This is a field too vast for any single person to understand in its entirety, and I talk mostly from a “amateur mathematician and computer scientist” standpoint.

    +

    Why is it hard to approach?

    +
      +
    • why it may be more difficult to approach than other, more recent areas like ML and DL +
        +
      • slightly longer history
        • +
      • always very close to applications: somehow more “messy” in its notations, vocabulary, standard references, etc, as other “purer” fields of maths (similar to stats in this regard)
        • +
      • often approached from a applied point of view means that many very different concepts are often mixed together
        • +
      • +
    • why it is interesting and you should pursue it anyway +
        +
      • history of the field
        • +
      • examples of applications
        • +
      • theory perspective, rigorous field
        • +
      • +
    • different subfields +
        +
      • optimisation: constrained and unconstrained
        • +
      • game theory
        • +
      • dynamic programming
        • +
      • stochastic processes
        • +
      • simulation
        • +
      • +
    • how to learn and practice +
        +
      • references
        • +
      • courses
        • +
      • computational assets
        • +
      • +
    +
    +
    + + +
    + Site proudly generated by + Hakyll +
    + + diff --git a/_site/rss.xml b/_site/rss.xml index 68ec24f..315b9c7 100644 --- a/_site/rss.xml +++ b/_site/rss.xml @@ -64,6 +64,52 @@ https://www.lozeve.com/posts/iclr-2020-notes.html Dimitri Lozeve + + Operations Research and Optimisation: where to start? + https://www.lozeve.com/posts/operations-research-references.html + +
    + +
    +
    +

    Operations research (OR) is a vast area comprising a lot of theory, different branches of mathematics, and too many applications to count. In this post, I will try to explain why I find it so fascinating, but also why it can be a little disconcerting to explore at first. Then I will try to ease the newcomer’s path in this rich area, by suggesting a very rough “map” of the field and a few references to get started.

    +

    Keep in mind that although I studied it during my graduate studies, this is not my primary area of expertise (I’m a data scientist by trade), and I definitely don’t pretend to know everything in OR. This is a field too vast for any single person to understand in its entirety, and I talk mostly from a “amateur mathematician and computer scientist” standpoint.

    +

    Why is it hard to approach?

    +
      +
    • why it may be more difficult to approach than other, more recent areas like ML and DL +
        +
      • slightly longer history
        • +
      • always very close to applications: somehow more “messy” in its notations, vocabulary, standard references, etc, as other “purer” fields of maths (similar to stats in this regard)
        • +
      • often approached from a applied point of view means that many very different concepts are often mixed together
        • +
      • +
    • why it is interesting and you should pursue it anyway +
        +
      • history of the field
        • +
      • examples of applications
        • +
      • theory perspective, rigorous field
        • +
      • +
    • different subfields +
        +
      • optimisation: constrained and unconstrained
        • +
      • game theory
        • +
      • dynamic programming
        • +
      • stochastic processes
        • +
      • simulation
        • +
      • +
    • how to learn and practice +
        +
      • references
        • +
      • courses
        • +
      • computational assets
        • +
      • +
    +
    + +]]>     + Wed, 08 Apr 2020 00:00:00 UT + https://www.lozeve.com/posts/operations-research-references.html + Dimitri Lozeve +     Reading notes: Hierarchical Optimal Transport for Document Representation https://www.lozeve.com/posts/hierarchical-optimal-transport-for-document-classification.html diff --git a/posts/operations-research-references.org b/posts/operations-research-references.org new file mode 100644 index 0000000..1900610 --- /dev/null +++ b/posts/operations-research-references.org @@ -0,0 +1,44 @@ +--- +title: "Operations Research and Optimisation: where to start?" +date: 2020-04-08 +--- + +[[https://en.wikipedia.org/wiki/Operations_research][Operations research]] (OR) is a vast area comprising a lot of theory, +different branches of mathematics, and too many applications to +count. In this post, I will try to explain why I find it so +fascinating, but also why it can be a little disconcerting to explore +at first. Then I will try to ease the newcomer's path in this rich +area, by suggesting a very rough "map" of the field and a few +references to get started. + +Keep in mind that although I studied it during my graduate studies, +this is not my primary area of expertise (I'm a data scientist by +trade), and I definitely don't pretend to know everything in OR. This +is a field too vast for any single person to understand in its +entirety, and I talk mostly from a "amateur mathematician and computer +scientist" standpoint. + +* Why is it hard to approach? + +- why it may be more difficult to approach than other, more recent + areas like ML and DL + - slightly longer history + - always very close to applications: somehow more "messy" in its + notations, vocabulary, standard references, etc, as other "purer" + fields of maths (similar to stats in this regard) + - often approached from a applied point of view means that many very + different concepts are often mixed together +- why it is interesting and you should pursue it anyway + - history of the field + - examples of applications + - theory perspective, rigorous field +- different subfields + - optimisation: constrained and unconstrained + - game theory + - dynamic programming + - stochastic processes + - simulation +- how to learn and practice + - references + - courses + - computational assets