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Non-linear optimization. An overview, problems and a guide. Optimization. Unconstraint non-linear optimization. E( w ). w 2. w 1. Classes of Methods. Linear optimization Constraint <-> unconstraint Gradient based 1 st order, 2 nd order Genetic Algorithms, Evolutionary Strategies
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Non-linear optimization An overview, problems and a guide
Optimization • Unconstraint non-linear optimization E(w) w2 w1
Classes of Methods • Linear optimization • Constraint <-> unconstraint • Gradient based • 1st order, 2nd order • Genetic Algorithms,Evolutionary Strategies • Stochastic methods (Simulated Annealing, Tabu Search, …)
Performance criteria • Number of function evaluations • Number of gradient calculation • Time • Number of fails • Number of method params. • Sensitivity of method params. • Accuracy
Methods • Direct methods • Successive variation • Hooke-Jeeves • Gradient based methods • Gradient decent • Back-propagation • Polak-Ribiere • Second order methods • Newton-Raphson • BFGS
Back-propagation Gradient decent Momentum
Back-propagation Error E Cycle
Conjugated gradients Qn property
Polak-Ribiere Beam search
Newton-method Q1 property
Comparison: Canyon-Function n(E)=8983
Comparison: Step-Function n(E)=2487 n(E)=2448
Decision tree Complexity #minima many some few one Knowledge MC / SA GA / ES Multi-start differentiable no yes elliptic? aligned? no yes no yes yes #parameters coordinate axis channels? G / PR/ BFGS many few no yes curved along axes flat NM / LBFGS HJ / ROS ROS SV #parameters QP / RPROP BP many few PR / LBFGS BFGS