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A more reliable reduction algorithm for behavioral model extraction. Dmitry Vasilyev, Jacob White Massachusetts Institute of Technology. Outline. Background Projection framework for model reduction Balanced Truncation algorithm and approximations AISIAD algorithm

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## A more reliable reduction algorithm for behavioral model extraction

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**A more reliable reduction algorithm for behavioral model**extraction Dmitry Vasilyev, Jacob White Massachusetts Institute of Technology**Outline**• Background • Projection framework for model reduction • Balanced Truncation algorithm and approximations • AISIAD algorithm • Description of the proposed algorithm • Modified AISIAD and a low-rank square root algorithm • Efficiency and accuracy • Conclusions**Model reduction problem**inputs outputs Many (> 104) internal states inputs outputs few (<100) internal states • Reduction should be automatic • Must preserve input-output properties**Differential Equation Model**- state A – stable, n xn (large) E – SPD, n xn - vector of inputs - vector of outputs • Model can represent: • Finite-difference spatial discretization of PDEs • Circuits with linear elements**Model reduction problem**n – large (thousands)! q – small (tens) Need the reduction to be automatic and preserve input-output properties (transfer function)**Approximation error**• Wide-band applications: model should have small worst-case error => maximal difference over all frequencies ω**Projection framework for model reduction**• Pick biorthogonal projection matrices W and V • Projection basis are columns of V and W x Vxr x x n q V xr Ax WTAVxr Most reduction methods are based on projection**Projection should preserve important modes**u y LTI SYSTEM t t input output P (controllability) Which modes are easier to reach? Q (observability) Which modes produce more output? X (state) • Reduced model retains most controllable and most observable modes • Mode must be both very controllable and very observable**Balanced truncation reduction (TBR)**Compute controllability and observability gramians P and Q : (~n3)AP + PAT + BBT =0 ATQ + QA + CTC = 0 Reduced model keeps the dominant eigenspaces of PQ : (~n3) PQvi= λiviwiPQ = λiwi Reduced system: (WTAV, WTB, CV, D) Very expensive. P and Q are dense even for sparse models**Most reduction algorithms effectively separately approximate**dominant eigenspaces of Pand Q: • Arnoldi [Grimme ‘97]:V = colsp{A-1B, A-2B, …}, W=VT, approx. Pdomonly • Padé via Lanczos [Feldman and Freund ‘95]colsp(V) = {A-1B, A-2B, …}, - approx. Pdomcolsp(W) = {A-TCT, (A-T )2CT, …},- approx. Qdom • Frequency domain POD [Willcox ‘02], Poor Man’s TBR [Phillips ‘04] colsp(V) = {(jω1I-A)-1B, (jω2I-A)-1B, …}, - approx.Pdom colsp(W) = {(jω1I-A)-TCT, (jω2I-A)-TCT, …},- approx.Qdom However, what matters is the product PQ**RC line (symmetric circuit)**V(t) – input i(t) - output • Symmetric, P=Qall controllable states are observable and vice versa**RLC line (nonsymmetric circuit)**Vector of states: • P and Q are no longer equal! • By keeping only mostly controllable and/or only mostly observable states, we may not find dominant eigenvectors of PQ**Lightly damped RLC circuit**R = 0.008, L = 10-5 C = 10-6 N=100 • Exact low-rank approximations of P and Q of order < 50 leads to PQ≈ 0!!**Lightly damped RLC circuit**Top 5 eigenvectorsof Q Top 5 eigenvectors of P Union of eigenspaces of P and Q does not necessarily approximate dominant eigenspace of PQ .**Xi= (PQ)Vi => Vi+1= qr(Xi)**“iterate” AISIAD model reduction algorithm Idea of AISIAD approximation: Approximate eigenvectors using power iterations: Viconverges to dominant eigenvectors ofPQ Need to find the product (PQ)Vi How?**Approximation of the product Vi+1=qr(PQVi), AISIAD algorithm**Wi≈ qr(QVi) Vi+1≈ qr(PWi) Approximate using solution of Sylvester equation Approximate using solution of Sylvester equation**More detailed view of AISIAD approximation**Right-multiply by Wi (original AISIAD) X H, qxq X M, nxq**Modified AISIAD approximation**Right-multiply by Vi ^ X H, qxq X Approximate! M, nxq**Modified AISIAD approximation**Right-multiply by Vi ^ X H, qxq X Approximate! M, nxq We can take advantage of numerous methods, which approximate P and Q!**Specialized Sylvester equation**-M X X A = + H qxq nxq nxn Need only column span of X**Solving Sylvester equation**Schur decomposition of H : -M X X A ~ ~ ~ = + ~ Solve for columns of X X**Solving Sylvester equation**• Applicable to any stable A • Requires solving q times Schur decomposition of H : Solution can be accelerated via fast MVP Another methods exists, based on IRA, needs A>0 [Zhou ‘02]**Solving Sylvester equation**• Applicable to any stable A • Requires solving q times Schur decomposition of H : ^ For SISO systems and P=0 equivalent to matching at frequency points –Λ(WTAW)**Modified AISIAD algorithm**LR-sqrt ^ ^ • Obtain low-rank approximations of Pand Q • Solve AXi+XiH+ M = 0, => Xi≈ PWi where H=WiTATWi, M = P(I - WiWiT)ATWi + BBTWi • Perform QR decomposition of Xi =ViR • Solve ATYi+YiF+ N = 0, => Yi≈ QVi where F=ViTAVi, N = Q(I - ViViT)AV + CTCVi • Perform QR decomposition of Yi =Wi+1 Rto get new iterate. • Go to step 2 and iterate. • Bi-orthogonalize WandVand construct reduced model: ^ ^ (WTAV, WTB, CV, D)**For systems in the descriptor form**Generalized Lyapunov equations: Lead to similar approximate power iterations**mAISIAD and low-rank square root**Low-rank gramians (cost varies) mAISIAD LR-square root (inexpensive step) (more expensive) For the majority of non-symmetric cases, mAISIAD works better than low-rank square root**RLC line example results**H-infinity norm of reduction error (worst-case discrepancy over all frequencies) N = 1000, 1 input 2 outputs**Steel rail coolling profile benchmark**Taken from Oberwolfach benchmark collection, N=1357 7 inputs, 6 outputs**mAISIAD is useless for symmetric models**For symmetric systems (A = AT, B = CT) P=Q, therefore mAISIAD is equivalent to LRSQRT for P,Q of order q ^ ^ RC line example**Cost of the algorithm**• Cost of the algorithm is directly proportional to the cost of solving a linear system:(where sjj is a complex number) • Cost does not depend on the number of inputs and outputs (non-descriptor case) (descriptor case)**Conclusions**• The algorithm has a superior accuracy and extended applicability with respect to the original AISIAD method • Very promising low-cost approximation to TBR • Applicable to any dynamical system, will work (though, usually worse) even without low-rank gramians • Passivity and stability preservation possible via post-processing • Not beneficial if the model is symmetric

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