Difference between revisions of "Lotka Experimental Design (AMPL)"
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### Fisher information matrix | ### Fisher information matrix | ||
− | # F1: w1 * G11*G11 + w2 * | + | # F1: w1 * G11*G11 + w2 * G21*G21 |
subject to dynamicF1{i in 1..nt}: | subject to dynamicF1{i in 1..nt}: | ||
− | F[1,i] = F[1,i-1] + h * ( w[1,uidx[i-1]] * G[1,1,i] * G[1,1,i] + w[2,uidx[i-1]] * G[ | + | F[1,i] = F[1,i-1] + h * ( w[1,uidx[i-1]] * G[1,1,i] * G[1,1,i] + w[2,uidx[i-1]] * G[2,1,i] * G[2,1,i]); |
− | # F2: w1 * G11* | + | # F2: w1 * G11*G12 + w2 * G21*G22 |
subject to dynamicF2{i in 1..nt}: | subject to dynamicF2{i in 1..nt}: | ||
− | F[2,i] = F[2,i-1] + h * ( w[1,uidx[i-1]] * G[1,1,i] * G[ | + | F[2,i] = F[2,i-1] + h * ( w[1,uidx[i-1]] * G[1,1,i] * G[1,2,i] + w[2,uidx[i-1]] * G[2,1,i] * G[2,2,i]); |
− | # F3: w1 * | + | # F3: w1 * G12*G12 + w2 * G22*G22 |
subject to dynamicF3{i in 1..nt}: | subject to dynamicF3{i in 1..nt}: | ||
− | F[3,i] = F[3,i-1] + h * ( w[1,uidx[i-1]] * G[ | + | F[3,i] = F[3,i-1] + h * ( w[1,uidx[i-1]] * G[1,2,i] * G[1,2,i] + w[2,uidx[i-1]] * G[2,2,i] * G[2,2,i]); |
### Summing up measurements | ### Summing up measurements |
Latest revision as of 17:18, 18 October 2012
This page contains a discretized version of the MIOCP Lotka Experimental Design in AMPL format. You should be aware of the comments regarding discretization made on the AMPL overview page.
AMPL
The model in AMPL code for a fixed control discretization grid with a collocation method, using an implicit Euler. We need a model file vplotka.mod,
# ---------------------------------------------------------------- # Lotka Experimental Design problem with collocation (implicit Euler) # (c) Sebastian Sager # ---------------------------------------------------------------- param nt > 0 integer; param nu > 0 integer; param ntperu > 0 integer; param nx > 0 integer; param np > 0 integer; param npar > 0 integer; param nF > 0 integer; param tf > 0; param h; param initialization; set I:= 0..nt; set U:= 0..nu-1; param uidx {I}; param p{1..np}; param M; param x0{1..nx}; param G0{1..nx,1..npar}; param F0{1..nF}; var x{1..nx,0..nt}; var G{1..nx,1..npar,0..nt}; var F{1..nF,0..nt}; var w{1..nx,0..nu-1} >= 0, <= 1; var u{0..nu-1} >= 0, <= 1; ### Calculate trace of 2x2 inverse matrix by hand and add penalization minimize covariance: (F[1,nt] + F[3,nt]) / ( F[1,nt]*F[3,nt] - F[2,nt]*F[2,nt]) + p[9] * 0.5 * h * ntperu * ( w[1,0] + w[2,0] + w[1,nu-1] + w[2,nu-1] + 2 * sum {i in U diff {0,nu-1} } ( w[1,i] + w[2,i] ) ); ### Predator prey dynamics subject to dynamicX1{i in 1..nt}: x[1,i] = x[1,i-1] + h * ( p[1]*x[1,i] - p[2]*x[1,i]*x[2,i] - p[5]*x[1,i]*u[uidx[i-1]] ); subject to dynamicX2{i in 1..nt}: x[2,i] = x[2,i-1] + h * ( -p[3]*x[2,i] + p[4]*x[1,i]*x[2,i] - p[6]*x[2,i]*u[uidx[i-1]] ); ### Variational differential equations ### Calculated by hand for par = (p2, p4) # fx11 = p[1] - p[2]*x[2,i-1] - p[5]*u[uidx[i-1]] # fx12 = - p[2]*x[1,i-1] # fx21 = p[4]*x[2,i-1] # fx22 = -p[3] + p[4]*x[1,i-1] - p[6]*u[uidx[i-1]] # fp11 = -x[1,i-1]*x[2,i-1] # fp22 = x[1,i-1]*x[2,i-1] # G11: fx11*G11 + fx12*G21 + fp11 subject to dynamicG11{i in 1..nt}: G[1,1,i] = G[1,1,i-1] + h * ( ( p[1] - p[2]*x[2,i] - p[5]*u[uidx[i-1]] ) * G[1,1,i] + ( - p[2]*x[1,i] ) * G[2,1,i] + ( -x[1,i]*x[2,i] ) ); # G12: fx11*G12 + fx12*G22 subject to dynamicG12{i in 1..nt}: G[1,2,i] = G[1,2,i-1] + h * ( ( p[1] - p[2]*x[2,i] - p[5]*u[uidx[i-1]] ) * G[1,2,i] + ( - p[2]*x[1,i] ) * G[2,2,i] ); # G21: fx21*G11 + fx22*G21 subject to dynamicG21{i in 1..nt}: G[2,1,i] = G[2,1,i-1] + h * ( ( p[4]*x[2,i] ) * G[1,1,i] + ( -p[3] + p[4]*x[1,i] - p[6]*u[uidx[i-1]] ) * G[2,1,i] ); # G22: fx21*G12 + fx22*G22 + fp22 subject to dynamicG22{i in 1..nt}: G[2,2,i] = G[2,2,i-1] + h * ( ( p[4]*x[2,i] ) * G[1,2,i] + ( -p[3] + p[4]*x[1,i] - p[6]*u[uidx[i-1]] ) * G[2,2,i] + ( x[1,i]*x[2,i] ) ); ### Fisher information matrix # F1: w1 * G11*G11 + w2 * G21*G21 subject to dynamicF1{i in 1..nt}: F[1,i] = F[1,i-1] + h * ( w[1,uidx[i-1]] * G[1,1,i] * G[1,1,i] + w[2,uidx[i-1]] * G[2,1,i] * G[2,1,i]); # F2: w1 * G11*G12 + w2 * G21*G22 subject to dynamicF2{i in 1..nt}: F[2,i] = F[2,i-1] + h * ( w[1,uidx[i-1]] * G[1,1,i] * G[1,2,i] + w[2,uidx[i-1]] * G[2,1,i] * G[2,2,i]); # F3: w1 * G12*G12 + w2 * G22*G22 subject to dynamicF3{i in 1..nt}: F[3,i] = F[3,i-1] + h * ( w[1,uidx[i-1]] * G[1,2,i] * G[1,2,i] + w[2,uidx[i-1]] * G[2,2,i] * G[2,2,i]); ### Summing up measurements subject to wSum{k in 1..nx}: sum{i in 0..nt-1} h*w[k,uidx[i]] <= p[6+k]; ### Initial values subject to iniX{k in 1..nx}: x[k,0] = x0[k]; subject to iniG{k in 1..nx, j in 1..npar}: G[k,j,0] = G0[k,j]; subject to iniF{k in 1..nF}: F[k,0] = F0[k];
a data file vplotka.dat,
# ------------------------------------ # Data: Lotka Experimental Design problem # ------------------------------------ # Change values here param ntperu := 10; param nu := 50; param nt := 500; param tf := 12; param nx := 2; param np := 9; param npar := 2; param nF := 3; param initialization := 1e-1; let x0[1] := 0.5; let x0[2] := 0.7; let {k in 1..nx, j in 1..npar} G0[k,j] := 0; let {k in 1..nF} F0[k] := 0; let F0[2] := 0; let p[1] := 1.0; let p[2] := 1.0; let p[3] := 1.0; let p[4] := 1.0; let p[5] := 0.4; let p[6] := 0.2; let p[7] := 4.0; let p[8] := 4.0; let p[9] := 0; # Set indices of controls corresponding to time points for {i in 0..nu-1} { for {j in 0..ntperu-1} { let uidx[i*ntperu+j] := i; } } let uidx[nt] := nu-1; # Initialization let h := tf / nt; let {i in 0..nu-1} u[i] := 0.3; let {k in 1..nx, i in 0..nu-1} w[k,i] := 1.0; let {k in 1..nx, i in 0..nt} x[k,i] := x0[k]; let {k in 1..nx, j in 1..npar, i in 0..nt} G[k,j,i] := initialization; # G0[k,j]; let {k in 1..nF, i in 0..nt} F[k,i] := initialization; # F0[k]; let {k in 2..2, i in 0..nt} F[k,i] := 0;
and a running script vplotka.run,
# ------------------------------------ # Solve Lotka Experimental Design problem # ------------------------------------ model vplotka.mod; data vplotka.dat; option solver ipopt; solve;