PICOS is a user friendly interface to several conic and integer programming solvers. PICOS is a user friendly interface to several conic and integer programming solvers, very much like YALMIP or CVX under MATLAB. The main motivation for PICOS is to have the possibility to enter an optimization problem as a high level model, and to be able to solve it with several different solvers. Multidimensional and matrix variables are handled in a natural fashion, which makes it painless to formulate a SDP or a SOCP. This is very useful for educational purposes, and to quickly implement some models and test their validity on simple examples. Furthermore, with PICOS you can take advantage of the python programming language to read and write data, construct a list of constraints by using python list comprehensions, take slices of multidimensional variables, etc.
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References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Diamond, Steven; Boyd, Stephen: CVXPY: a Python-embedded modeling language for convex optimization (2016)
- Dias, Gustavo; Liberti, Leo: Diagonally dominant programming in distance geometry (2016)
- Friberg, Henrik A.: CBLIB 2014: a benchmark library for conic mixed-integer and continuous optimization (2016)
- Sagnol, Guillaume; Harman, Radoslav: Computing exact $D$-optimal designs by mixed integer second-order cone programming (2015)
- Wittek, Peter: Algorithm 950: Ncpol2sdpa -- sparse semidefinite programming relaxations for polynomial optimization problems of noncommuting variables (2015)
- Sagnol, Guillaume: On the semidefinite representation of real functions applied to symmetric matrices (2013)