Second-order cone programming

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A second-order cone program (SOCP) is a convex optimization problem of the form

minimize
subject to

where the problem parameters are , and . Here is the optimization variable. [1] When for , the SOCP reduces to a linear program. When for , the SOCP is equivalent to a convex quadratically constrained linear program. Quadratically constrained quadratic programs can also be formulated as SOCPs by reformulating the objective function as a constraint. Semidefinite programming subsumes SOCPs as the SOCP constraints can be written as linear matrix inequalities (LMI) and can be reformulated as an instance of semi definite program. SOCPs can be solved with great efficiency by interior point methods.

Example: Quadratic constraint[edit]

Consider a quadratic constraint of the form

This is equivalent to the SOC constraint

Example: Stochastic linear programming[edit]

Consider a stochastic linear program in inequality form

minimize
subject to

where the parameters are independent Gaussian random vectors with mean and covariance and . This problem can be expressed as the SOCP

minimize
subject to

where is the inverse normal cumulative distribution function.[1]

Example: Stochastic second-order cone programming[edit]

We refer to second-order cone programs as deterministic second-order cone programs since data defining them are deterministic. Stochastic second-order cone programs[2] is a class of optimization problems that defined to handle uncertainty in data defining deterministic second-order cone programs.

Solvers and scripting (programming) languages[edit]

Name License Brief info
AMPL commercial An algebraic modeling language with SOCP support
CPLEX commercial
Gurobi commercial parallel SOCP barrier algorithm
MOSEK commercial

References[edit]

  1. ^ a b Boyd, Stephen; Vandenberghe, Lieven (2004). Convex Optimization (pdf). Cambridge University Press. ISBN 978-0-521-83378-3. Retrieved October 3, 2011. 
  2. ^ Alzalg, Baha (2012). "Stochastic second-order cone programming: Application models". Applied Mathematical Modelling. 36 (10): 5122–5134. doi:10.1016/j.apm.2011.12.053.