Add solvers
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</span></span>. It contains an interesting overview of deep learning and integer programming, with a focus on connections, and applications to recent research areas in ML (reinforcement learning, attention, etc.).</p>
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<h2 id="solvers-and-computational-resources">Solvers and computational resources <span id="solvers"></span></h2>
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<p>When you start reading about modelling and algorithms, I recommend you try solving a few problems yourself, either by hand for small instances, or using an existing solver. It will allow you to follow the examples in books, while also practising your modelling skills. You will also get an intuition of what is difficult to model and to solve.</p>
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<p>There are many solvers available, both free and commercial, with various capabilities. I recommend you use the fantastic <a href="https://github.com/JuliaOpt/JuMP.jl">JuMP</a> library for Julia, which exposes a domain-specific language for modelling, along with interfaces to nearly all major solver packages. (Even if you don’t know Julia, this is a great and easy way to start!) If you’d rather use Python, you can use Google’s <a href="https://developers.google.com/optimization/introduction/python">OR-Tools</a> or <a href="https://github.com/coin-or/pulp">PuLP</a> for linear programming.</p>
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<p>Regarding solvers, there is a <a href="http://www.juliaopt.org/JuMP.jl/stable/installation/#Getting-Solvers-1">list of solvers</a> on JuMP’s documentation, with their capabilities and their license. Free solvers include <a href="https://www.gnu.org/software/glpk/">GLPK</a> (linear programming), <a href="https://github.com/coin-or/Ipopt">Ipopt</a> (non-linear programming), and <a href="https://scip.zib.de/">SCIP</a> (mixed-integer linear programming).</p>
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<p>Commercial solvers often have better performance, and some of them propose a free academic license: <a href="https://www.mosek.com/">MOSEK</a>, <a href="https://www.gurobi.com/">Gurobi</a>, and <a href="https://www.ibm.com/analytics/cplex-optimizer">IBM CPLEX</a> in particular all offer free academic licenses and work very well with JuMP.</p>
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<p>Another awesome resource is the <a href="https://neos-server.org/neos/">NEOS Server</a>. It offers free computing resources for numerical optimization, including all major free and commercial solvers! You can submit jobs on it in a standard format, or interface your favourite programming language with it. The fact that such an amazing resource exists for free, for everyone is extraordinary. They also have an accompanying book, the <a href="https://neos-guide.org/">NEOS Guide</a>, containing many case studies and description of problem types. The <a href="https://neos-guide.org/content/optimization-taxonomy">taxonomy</a> may be particularly useful.</p>
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<h2 id="references" class="unnumbered">References</h2>
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<div id="refs" class="references">
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<div id="ref-bertsimas1997_introd">
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@ -87,6 +87,11 @@
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<br />
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</span></span>. It contains an interesting overview of deep learning and integer programming, with a focus on connections, and applications to recent research areas in ML (reinforcement learning, attention, etc.).</p>
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<h2 id="solvers-and-computational-resources">Solvers and computational resources <span id="solvers"></span></h2>
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<p>When you start reading about modelling and algorithms, I recommend you try solving a few problems yourself, either by hand for small instances, or using an existing solver. It will allow you to follow the examples in books, while also practising your modelling skills. You will also get an intuition of what is difficult to model and to solve.</p>
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<p>There are many solvers available, both free and commercial, with various capabilities. I recommend you use the fantastic <a href="https://github.com/JuliaOpt/JuMP.jl">JuMP</a> library for Julia, which exposes a domain-specific language for modelling, along with interfaces to nearly all major solver packages. (Even if you don’t know Julia, this is a great and easy way to start!) If you’d rather use Python, you can use Google’s <a href="https://developers.google.com/optimization/introduction/python">OR-Tools</a> or <a href="https://github.com/coin-or/pulp">PuLP</a> for linear programming.</p>
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<p>Regarding solvers, there is a <a href="http://www.juliaopt.org/JuMP.jl/stable/installation/#Getting-Solvers-1">list of solvers</a> on JuMP’s documentation, with their capabilities and their license. Free solvers include <a href="https://www.gnu.org/software/glpk/">GLPK</a> (linear programming), <a href="https://github.com/coin-or/Ipopt">Ipopt</a> (non-linear programming), and <a href="https://scip.zib.de/">SCIP</a> (mixed-integer linear programming).</p>
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<p>Commercial solvers often have better performance, and some of them propose a free academic license: <a href="https://www.mosek.com/">MOSEK</a>, <a href="https://www.gurobi.com/">Gurobi</a>, and <a href="https://www.ibm.com/analytics/cplex-optimizer">IBM CPLEX</a> in particular all offer free academic licenses and work very well with JuMP.</p>
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<p>Another awesome resource is the <a href="https://neos-server.org/neos/">NEOS Server</a>. It offers free computing resources for numerical optimization, including all major free and commercial solvers! You can submit jobs on it in a standard format, or interface your favourite programming language with it. The fact that such an amazing resource exists for free, for everyone is extraordinary. They also have an accompanying book, the <a href="https://neos-guide.org/">NEOS Guide</a>, containing many case studies and description of problem types. The <a href="https://neos-guide.org/content/optimization-taxonomy">taxonomy</a> may be particularly useful.</p>
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<h2 id="references" class="unnumbered">References</h2>
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<div id="refs" class="references">
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<div id="ref-bertsimas1997_introd">
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@ -54,6 +54,11 @@
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<br />
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</span></span>. It contains an interesting overview of deep learning and integer programming, with a focus on connections, and applications to recent research areas in ML (reinforcement learning, attention, etc.).</p>
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<h2 id="solvers-and-computational-resources">Solvers and computational resources <span id="solvers"></span></h2>
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<p>When you start reading about modelling and algorithms, I recommend you try solving a few problems yourself, either by hand for small instances, or using an existing solver. It will allow you to follow the examples in books, while also practising your modelling skills. You will also get an intuition of what is difficult to model and to solve.</p>
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<p>There are many solvers available, both free and commercial, with various capabilities. I recommend you use the fantastic <a href="https://github.com/JuliaOpt/JuMP.jl">JuMP</a> library for Julia, which exposes a domain-specific language for modelling, along with interfaces to nearly all major solver packages. (Even if you don’t know Julia, this is a great and easy way to start!) If you’d rather use Python, you can use Google’s <a href="https://developers.google.com/optimization/introduction/python">OR-Tools</a> or <a href="https://github.com/coin-or/pulp">PuLP</a> for linear programming.</p>
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<p>Regarding solvers, there is a <a href="http://www.juliaopt.org/JuMP.jl/stable/installation/#Getting-Solvers-1">list of solvers</a> on JuMP’s documentation, with their capabilities and their license. Free solvers include <a href="https://www.gnu.org/software/glpk/">GLPK</a> (linear programming), <a href="https://github.com/coin-or/Ipopt">Ipopt</a> (non-linear programming), and <a href="https://scip.zib.de/">SCIP</a> (mixed-integer linear programming).</p>
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<p>Commercial solvers often have better performance, and some of them propose a free academic license: <a href="https://www.mosek.com/">MOSEK</a>, <a href="https://www.gurobi.com/">Gurobi</a>, and <a href="https://www.ibm.com/analytics/cplex-optimizer">IBM CPLEX</a> in particular all offer free academic licenses and work very well with JuMP.</p>
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<p>Another awesome resource is the <a href="https://neos-server.org/neos/">NEOS Server</a>. It offers free computing resources for numerical optimization, including all major free and commercial solvers! You can submit jobs on it in a standard format, or interface your favourite programming language with it. The fact that such an amazing resource exists for free, for everyone is extraordinary. They also have an accompanying book, the <a href="https://neos-guide.org/">NEOS Guide</a>, containing many case studies and description of problem types. The <a href="https://neos-guide.org/content/optimization-taxonomy">taxonomy</a> may be particularly useful.</p>
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<h2 id="references" class="unnumbered">References</h2>
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<div id="refs" class="references">
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<div id="ref-bertsimas1997_introd">
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@ -180,4 +180,38 @@ problem in a readable form.
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* Solvers and computational resources <<solvers>>
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When you start reading about modelling and algorithms, I recommend you
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try solving a few problems yourself, either by hand for small
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instances, or using an existing solver. It will allow you to follow
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the examples in books, while also practising your modelling
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skills. You will also get an intuition of what is difficult to model
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and to solve.
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There are many solvers available, both free and commercial, with
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various capabilities. I recommend you use the fantastic [[https://github.com/JuliaOpt/JuMP.jl][JuMP]] library
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for Julia, which exposes a domain-specific language for modelling,
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along with interfaces to nearly all major solver packages. (Even if
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you don't know Julia, this is a great and easy way to start!) If you'd
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rather use Python, you can use Google's [[https://developers.google.com/optimization/introduction/python][OR-Tools]] or [[https://github.com/coin-or/pulp][PuLP]] for linear
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programming.
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Regarding solvers, there is a [[http://www.juliaopt.org/JuMP.jl/stable/installation/#Getting-Solvers-1][list of solvers]] on JuMP's documentation,
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with their capabilities and their license. Free solvers include [[https://www.gnu.org/software/glpk/][GLPK]]
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(linear programming), [[https://github.com/coin-or/Ipopt][Ipopt]] (non-linear programming), and [[https://scip.zib.de/][SCIP]]
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(mixed-integer linear programming).
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Commercial solvers often have better performance, and some of them
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propose a free academic license: [[https://www.mosek.com/][MOSEK]], [[https://www.gurobi.com/][Gurobi]], and [[https://www.ibm.com/analytics/cplex-optimizer][IBM CPLEX]] in
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particular all offer free academic licenses and work very well with
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JuMP.
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Another awesome resource is the [[https://neos-server.org/neos/][NEOS Server]]. It offers free computing
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resources for numerical optimization, including all major free and
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commercial solvers! You can submit jobs on it in a standard format, or
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interface your favourite programming language with it. The fact that
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such an amazing resource exists for free, for everyone is
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extraordinary. They also have an accompanying book, the [[https://neos-guide.org/][NEOS Guide]],
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containing many case studies and description of problem types. The
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[[https://neos-guide.org/content/optimization-taxonomy][taxonomy]] may be particularly useful.
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* References
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