prod_inv(x) \(\prod_i x_i^{-1}\)when\(x\)is positive;\(+\infty\)otherwise. Convex and nonincreasing. quad_form(x,P) \(x^TPx\)for real\(x\)and symmetric\(P\), and\(x^HPx\)for complex\(x\)and Hermitian\(P\). Convex in\(x\)for\(P\)constant and positive semidefinite...
R_itera=B*log2(1+gama); cvx_begin variable P(2,M) expression S for i=1:1:M gama_inv1(i)=P(1,i).*h_au(i)*N2+P(2,i).*h_ub(i)*N1+N1*N2 .*(prod_inv([P(1,i) P(2,i)]).*h_ub(i).*h_au(i)); end S=(gama.^2 *log2(exp(1)) ./ (2*(gama+1))) .*...
prod_inv(x) \prod_i x_i^{-1}whenxis positive;+\inftyotherwise. Convex and nonincreasing. quad_form(x,P) x^TPxfor realxand symmetricP, andx^HPxfor complexxand HermitianP. Convex inxforPconstant and positive semidefinite; concave inxforPconstant and negative semidefinite. ...
cvx_begin expression gama1_inv variable P(2,M) for m=1:1:100 gama1_inv(m)=N1.inv_pos(P(1,m).h_au(m)) +N2.inv_pos(P(2,m).h_ub(m)) +(N1N2).prod_inv([P(1,m) P(2,m)])./(h_au(m).h_ub(m)); end S=(((gama.^2)log2(exp(1)))./(2(gama+1))).(gam...
Can’t I just tell CVX somehow that my model is convex? No, you can’t. To understand why, you must understand that theDCP rulesetis not for your benefit. Yes, we want you to be able to prove that your model is convex, and the rules can certainly help. But in fact, the rulesar...