mpc_slides.dvi
Model Predictive Control • linear convex optimal control • finite horizon approximation • model predictive control • fast MPC implementations • supply chain management Prof. S. Boyd, EE364b, Stanford University Linear time-invariant convex optimal control minimize J = P∞ t=0 ℓ(x(t), u(t)) subject to u(t) ∈ U, x(t) ∈ X, t = 0, 1, . . . x(t + 1) = Ax(t) + Bu(t), t = 0, 1, . . . x(0) = z. • variables
ADMM
The alternating direction method of multipliers (ADMM) is an algorithm that solves convex optimization problems by breaking them into smaller pieces, each of which are then easier to handle. It has recently found wide application in a number of areas. On this page, we provide a few links to to interesting applications and implementations of the method, along with a few primary references. ADMM is
Alternating Direction Method of Multipliers
Alternating Direction Method of Multipliers Prof S. Boyd EE364b, Stanford University source: Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers (Boyd, Parikh, Chu, Peleato, Eckstein) 1 Goals robust methods for ◮ arbitrary-scale optimization – machine learning/statistics with huge data-sets – dynamic optimization on large-scale network ◮ decentrali
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