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Decomposable Optimisation Methods

This reading group session discusses the basics of convex optimization, including convex functions, unique minimum over convex domains, (sub)gradient methods, and decomposition methods. It also explores constrained convex optimization, optimality conditions, and network utility maximization. Implementation issues and the concept of primal-dual variables are also discussed.

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Decomposable Optimisation Methods

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  1. DecomposableOptimisation Methods LCA Reading Group, 12/04/2011 Dan-Cristian Tomozei

  2. Convexity • Convex function • Unique minimum over convex domain

  3. Roadmap • (Sub)Gradient Method • Convex Optimisationcrash course • NUM • Basic Decomposition Methods • Implicit Signalling

  4. Roadmap • (Sub)Gradient Method • Convex Optimisationcrash course • NUM • Basic Decomposition Methods • Implicit Signalling

  5. (Sub)gradient method • Unconstrained convex optimisation problem • If objective is differentiable, • Else, • Gain sequence • Constant • Diminishing

  6. Roadmap • (Sub)Gradient Method • Convex Optimisationcrash course • NUM • Basic Decomposition Methods • Implicit Signalling

  7. Constrained Convex Optimisation • “Primal” formulation • Convex constraints  unique solution • Lagrangian • “Dual” function • For all “feasible” points – lower bound • Slater’s condition  zero duality gap

  8. Optimality conditions • “Primal” and “dual” formulations • Karush-Kuhn-Tucker (KKT) Primal variables Dual variables (i.e., Lagrange multipliers) Optimum

  9. Roadmap • (Sub)Gradient Method • Convex Optimisation crash course • NUM • Basic Decomposition Methods • Implicit Signalling

  10. Network Utility Maximisation • Population of users • Concave utility functions (e.g., rates) • Typical formulation (e.g., [Kelly97]): • Network flows of rates • Physical links of max capacity • Routing matrix • Dual variables = congestion shadow prices

  11. Roadmap • (Sub)Gradient Method • Convex Optimisation crash course • NUM • Basic Decomposition Methods • Implicit Signalling

  12. Dual Decomposition • Coupling constraint • To decouple – simply write the dual objective • Iterative dual algorithm: • Each user computes • Use a gradient method to update dual variables, e.g.,

  13. Primal Decomposition • Coupling variable • To decouple – consider fixed coupling variable • Iterative primal algorithm: • Solve individual problems and get partial optima • Update primal coupling variable using gradient method

  14. Implementation issues • Certain problems can be decoupled • Dual decomposition  dual algorithm • Primal vars (rates) depend directly on dual vars (prices) • Price adaptation relies on current rates • Always closed form? • Primal decomposition • The other way around… • Do we really need to keep track of both primal and dual variables? Can duals be “measured” instead?

  15. Roadmap • (Sub)Gradient Method • Convex Optimisation crash course • NUM • Basic Decomposition Methods • Implicit Signalling

  16. Multipath unicast min-cost live streaming • Graph • Supported rate region • Network cost function • Unsupported rate allocation  • Marginal cost positive and strictly increasing • Source s wants to send data to receiver r at rate  at minimum cost • Supported  min-cut is at least

  17. Optimisation formulation • Write Lagrangian • Primal-dual provably converges to optimum

  18. Is it that hard? • Recall • Dual variables have queue-like evolution! • We already queue packets!

  19. Implicit Primal-Dual • Rate control via • Rate on link (i,j) • Increase prop to backlog difference • Decrease prop to marginal cost (measurable – RTT, …) • Influence of parameter s • Small  closer optimal allocation, huge queue sizes • Large  manageable queue sizes, optimality trade-off

  20. Conclusion • Finding a fit-all recipe is hard • We can handle some cases • Specific formulations may lead to nice protocols • See also • R. Srikant’s “Mathematics of Internet Congestion Control” • Kelly, Mauloo, Tan - *** • Palomar, Chiang - ***

  21. Questions

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