Solution quadratic problem with disjoint simplices constraints.
This package aims to solve the following quadratic problem
subject to disjoint simplices constraints. The problem is tackled by solving the dual problem
by using different iterative methods based on the standard subgradient method. In particular, we iteratively compute
and then update based on three different subgradient rules derived from the following paper
where is the projection over the non-negative orthant with
is the subgradient
is the full outer product of the subgradient
is an approximation of the hessian
is the average of the subgradient until and finally
is the chosen stepsize.
To check the derived rules and the theoretical analysis, check report
The code is entirely contained in a single script, to reduce allocation and execution time of Julia. To test the code, clone this repository, open your Julia REPL and run
]
to enter in pkg mode
instantiate
to download modules declared in Manifest.toml. Then
include(src/main.jl)
to compile the code in the REPL.
initialize(params)
will execute the program, asking for the problem dimension and the number of disjoint simplices .
params are the following:
- stepsize: the stepsize that you want to adopt;
- rule: one among the three update rules for described above;
- : hyperparameter for stepsize 2 and 3;
- : hyperparameter for stepsize 2 and 3;
- : hyperparameter for rule 2 and 3;
- h: hyperparameter for stepsize 0 and 1;
- max_iter: maximum iterations;
Below are two examples of results obtained with constant length and constant step stepsizes, using the different update rules of on a problem of size 5000 and number of constraints 2500