Prior to version 1.2, the functionsexp,log,log_det, and other functions from the exponential family could not be used within CVX. Until recently, CVX utilized so-called symmetric primal/dual solvers that simply cannot support those functions natively[4]. More recently, solvers such as Mosek hav...
log_det log of determinant of a positive definite matrix, \(\log \det(X)\). When used inside a CVX specification, log_det constrains its argument to be symmetric (if real) or Hermitian (if complex) and positive definite. With numerical argument, log_det returns -Inf if these constraints...
CVXQUAD: How to use CVXQUAD's Pade Approximant instead of CVX's unreliable Successive Approximation for GP mode, log, exp, entr, rel_entr, kl_div, log_det, det_rootn, exponential cone. CVXQUAD's Quantum (Matrix) Entropy & Matrix Log related functions ...
Log-Sum-Exp: $log\sum{e^{x_i}}$ 他是max函数的一个解析近似; 几何平均数: $f(x) = (\prod_{i=1}^N\ x_i)^{1/N}$, 是凹函数, $domf = \mathbb{R_{++}^n}$ 对数绝对值函数: $f(x) = log\ det(X)$, $domf = \mathbb{S_{++}^n} $ (可以用第二个等价条件切换到一维证...
those CVX files redirect togeo_meanandlog_det. Which means that if YALMIP callsgeomeanandlogdet, that would be converted into call to CVX’sgeo_meanandlog_det, and that would not end nicely. After removinggeomean.mandlogdet.m, you will still havegeo_meanandlog_det, which is what you ...
a=−1,b=2: Very classic:quadratic over linear is convex. a=b=1/2: Very classic:geometric mean is concave. a=b=1: neither convex nor concave! 2.Log-sum is convex 3.Log-det is concave on PSD cone 4.General geometric mean(∏xi)1nis concave ...
log_det log of determinant of a positive definite matrix,\log \det(X). When used inside a CVX specification,log_detconstrains its argument to be symmetric (if real) or Hermitian (if complex) and positive definite. With numerical argument,log_detreturns-Infif these constraints are not met. ...
as -log_det (A)<t, (t is a constant). Matlab always shows the same error as follows. I don’t understand the reason, because I have checked that there is not problem with matrix dimension. And for example, as a test, when I change log_det (A) to trace (A), there is not an...
(d); Tmp=log_det(0.5*((alf*sum(p)*trace(Hj'*W1*Hj)+sigma2)+(alf*sum(p)*trace(Hj'*W1*Hj)+sigma2)'))-t_j(d)*alf*sum(p(pi_numel))*real(trace(Hj'*W1*Hj))+log_det(0.5*(t_j(d)+t_j(d)'))+1; cost=cost+Tmp; numel_SINR(1,d)=p(d)*alf*trace(Hj'*W1*Hj); ...
Factor - setup time : 0.00 dense det. time : 0.00 Factor - ML order time : 0.00 GP order time : 0.00 Factor - nonzeros before factor : 1472 after factor : 1614 Factor - dense dim. : 0 flops : 1.53e+04 ITE PFEAS DFEAS GFEAS PRSTATUS POBJ DOBJ MU TIME ...