The main aim here is to optimize the trajectory of UAV for 3-time slots. Since I know the initial position of UAV at the beginning, I manually brought the distances (U, V) at the first time slot, but I want to optimize the (U, V) to bring me the maximum OBJ. while having quadratic constraints. the problem is the error that appears
incorrect log-convex - log-convex, Is there any way to have a quadratic function consisting of log-convex value?
I tried to use a quad_form and Sum_Square functions, but an error appears saying that I need an affine expression.
Here is my code for clarification:
OBJ=((Bo/(Ao0.693))norm([80 power(2,norm([Qx Qy-70]))])+(Co/(Ao0.693))*norm([40 power(2,norm([Qx Qy]))]));
U=norm([80 power(2,norm([Qx_new Qy_new-70]))]);
V=norm([40 power(2,norm([Qx_new Qy_new]))])
power (2,norm([Qx_new-Qx_old Qy_new-Qy_old]))<=25;
There is no log found in your code and I don’t know what you meaned. Please show the mathematical function you want to express and prove its convexity. As for your code, the OBJ should be defined as expression OBJ before it is used; The maximize(sum(OBJ(:))); might be better placed before subject to.
Here is the full code including Ao, Bo, Co. clc
%Placement of Ground User
and also here is the mathematical problem that I try to optimize
replacing every Dug [n] with U,
and every Dur[n] with V.
I searched the code to see how the model contains a log function, but I can’t find any. The thing is when I run the code, he considers that this model will be solved by a successive approximation method