There are plenty of courses and books out there. For convex optimization I'd take a look into Boyd's & Vandenberghe's lecture which also has a good accompanying script. The lecture/book from ...

Is my approach suitable to use a warm start into the model? If I understand you correctly, you first compute some solution, either by using the first feasible solution you get (variant 1) or by ...

You can use the linear constraint function to add additional constraints which prohibit mutually exclusive pairs. I'd imagine that each mini package has to be scheduled on one date so this leads to ...

In general no, these problems are hard. BUT: You might want to look into totally unimodular matrices and total dual integrality but this requires additional assumptions on the matrix or the problem ...

The problem here seems to be that the data is provided in a somewhat untidy fashion. To use the function the data needs to be preprocessed to extract each entry from the column "Predecessor(s)". You ...

You just need to sum over another index variable than $t$. Here is the correct code: from __future__ import division from pyomo.environ import * from pyomo import environ as pym model = ConcreteModel(...

I'd say that you are comparing software packages with very different aims and capabilities. Tensorflow was developed to solve problems which involve (deep) neural nets. Mosek and gurobi on the other ...

This seems like some sort of knapsack problem: Suppose you have a set of purchases and a certain amount of points. Each purchase can be "paid" by points as a whole, no partial usage of points for each ...

In the context of gas networks my former colleagues used (and still use) adaptive linearization techniques combined with other (sometimes nonlinear) heuristics. There is a whole book about the topic ...

Usually in the context of LP/MIP you make statements about the convex hull of the mixed-integer set that you defined $P = \text{conv}(\left\{x \in \mathbb{R}^{n} : A x \leq b, x_{j} \in \mathbb{Z} \...

Introduce new binary variable $s_t$ for each timebucket $t$ to the problem. Set objective coefficient of $s_t$ to $FC$ (your fixed costs). This variable indicates whether some product is produced in ...

In addition to the answer from @Mehdi... I've recently started to work with OR-tools and find it very nice for prototyping. The Python interface allowed me to produce a first version of my model ...

If you problem is continuous I would say that it might be beneficial. For problems that involve discrete variables I've not seen anything that does benefit from the usage of a GPU. GPUs aid problem ...

You should have a look at the work of Mathias Stolpe. He has done a lot of modelling for structural optimization problems with integer constraints. See for example his PhD thesis. As you already ...

I would rephrase the model a bit not using OP's matrix formulation. Instead of a matrix I will use binary variables $z_{st}$ indicating whether a service point $s$ is active in time slot $t$. Using ...

In open source codes you usually have more flexibility to change the parameter directly compared to commercial solvers. Take SCIP for example where you can program your own node selector rules. You ...

A nice comprehensive collection on applications can be found in the book by Desaulniers, Desrosiers and Solomon: Column Generation. It features articles about Shortest Path Problems with Resource ...

In addition to @Michael's comment you have to distinguish between the algorithm used to solve the root node of a problem and the algorithm used for the nodes in the branch-and-bound tree. gurobi (and ...

To the best of my knowledge the indicator constraints are just syntactic sugar for the user. Internally these indicator constraints are reformulated using computed big-M formulations or SOS ...

In addition to @Richard's answer: The time for setting up the model, i.e. declaring variables and constraints, surely depends on the programming language you use. After the model is ready, solving ...

RL (and Machine Learning in general) often use specialized forms of continuous optimization problems that enable one to use elegant solving techniques (e.g. training/learning on the GPU or even ...

From a pure optimization point of view you can say that it is possible to transform a linear fractional problem into a linear one (see for example here). In your case, this seems not feasible, as you ...

If you cannot use Concorde as suggested by Albert, I'd suggest you look for an implementation of the Lin-Kerninghan-Heuristic (which is also included in Concorde). It iterates between 2-opt and 3-opt ...