Alternating direction method of multipliers

ADMM problem form (with $f$, $g$ convex):
minimize $f(x)+g(z)$
subject to $Ax + Bz = c$

two sets of variables, with separable objective

augmented Lagrangian:

ADMM:

• $x^{k+1} := \arg \min_x L_\rho (x,z^k,y^k)$ // $x$-minimization
• $z^{k+1} := \arg \min_z L_\rho (x^{k+1},z,y^k)$ // $z$-minimization
• $y^{k+1} := y^k + \rho(Ax^{k+1} + Bz^{k+1}-c)$ // dual update

• flexible optimization algorithm that can handle many convex optimization problems
  • Represent different terms of the objective function using additional variables and introducing constraints
• Built on the Lagrangian multiplier method
• Solve the dual problem
• Add quadratic penalty (method of multipliers) to ease update of the dual variable and be more robust

#incomplete

linear programming? optimization? Convex Optimization notes

References:

1. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers (Boyd, Parikh, Chu, Peleato, Eckstein) https://web.stanford.edu/~boyd/papers/pdf/admm_distr_stats.pdf
   • slides: Boyd. Alternating Direction Method of Multipliers. EE364b. https://web.stanford.edu/~boyd/papers/pdf/admm_slides.pdf
   • https://stanford.edu/~boyd/admm.html
2. https://www.stat.cmu.edu/~ryantibs/convexopt/lectures/admm.pdf
3. https://paperswithcode.com/method/admm
4. https://www.stat.cmu.edu/~ryantibs/convexopt-F18/lectures/admm.pdf