MATLAB toolbox for optimization modeling
Official homepage https://yalmip.github.io
MATLAB toolbox for optimization modeling
Home Page: https://yalmip.github.io/
License: Other
MATLAB toolbox for optimization modeling
Official homepage https://yalmip.github.io
Generalize code from IPOPT interface
Reverse the login in compileinterfacedata when checking for which solver this not should be done. reverese to for which it should be done
To reproduce
sdpvar x y
saveampl([complements(x>=0,z>=0),0<=x<=1,0<=z<=2],-x-z,'amplfail.mod');
Hence, if semidefinite cuts are involved, a upper bound solver such as fmincon will not be picked, even though the problem is a simple nonlinear program, without the cut
line #124 should be changed from
[F_eq_left,F_eliminate_equality] = filter_eliminatation(F_eq,w,0);
to
[F_eq_left,F_eliminate_equality] = filter_eliminatation(F_eq,w,0, ops);
log2(1+sdpvar(2))
Error using slog (line 23)
SLOG only defined for vector arguments
Attach solver definitions with test-scripts to be run to detect issues etc. A field is already there, just has to be populated, and some layer around it to make it useful
Correctly detects non-convexity
sdpvar x
p = max(exp(x),-x);
sdpsettings('allownonconvex',0);
[F,failure,cause] = expandmodel([-1 <= x <= 1],-p,ops);
But not here!
[F,failure,cause] = expandmodel([],-p,ops);
Some solvers are strict with 255 chars per line, so code should break up long expressions.
clear all
clc
X = sdpvar(4,4);
% Imposing psd on full X: Works fine.
% Imposing psd on a few submatrices only
submat1 = [1 2 3]; submat2 = [1 2 4];
F = [X(submat1,submat1) >= 0];
F = [F, X(submat2,submat2) >= 0];
% Some random additional constraints
F = [F, X(1,2) == 2];
obj = trace(X);
ops = sdpsettings('dualize',1);
solvesdp(F,obj,ops);
Trying to do
A = sdpvar(8760/4,40)
B = kron(A, ones(4,1))
is very slow, compared to
A = randn(8760/4,40)
B = kron(A, ones(4,1))
Make it granular, such as no, partial solution, complete solution
Warn if user tries to assign initial solution without support
x = sdpvar(2,1);
a = sdpvar
solvesdp([x'*(eye(2)*(1+a))*x <= a,uncertain(a),-.1 <= a <= .1],sum(x))
sdpvar x
plot([-1 <= x <= 0])
Actual behavior (with e227e): no figure produced
Expected behavior: plot the 1D set
After an sdpvar is loaded from disk, YALMIP stops to function properly.
To reproduce (Matlab R2011a, YALMIP e227e5):
sdpvar x
save x
clear
load x
clear
sdpvar y
double(y)
??? Cell contents reference from a non-cell array object.
Error in ==> yalmip at 617
varargout{1} = internal_sdpvarstate.optSolution{internal_sdpvarstate.activeSolution};
Error in ==> sdpvar.double at 37
solution = yalmip('getsolution');
This causes a bug
[0<=1, x>=0]
This doesn't!
[x>=0,0<=1]
sdpvar x
plot([ x <= 0; x >= 1])
ans =
{[]}
You should put something like this when exiting lmi/plot:
if nargout==0
clear x_opt
end
Try to avoid to bilinearize models in bmibn if possible (nonlinear solver supports polynomials etc)
When variables are eliminated feasibility is incorrectly determined
sdpvar x
sdpvar z
sdpvar y
P = optimizer([-1 <= y <= 1,x <= 10+z*x],x^2,[],[y;z],x)
[~,err] = P{[0;5]};
err
The reason is that the ordering is messed up. Internally somewhere, it is assumed the parametric variables are listed in the same order as they were defined
Implement internal GEVP solver, automatically extracting the GEVP model from a bilinear SDP
dx = sdpvar(15,1);
cumsum(dx)
ooqp reports success, but YALMIP reports time limit exceeded
Error happens when the variable eae=0.2, but replace with 0.21 and it works. Seems to generate a quadratic with very small negative eigenvalues, thus triggering the code to treat marginally indefinite problems as positive semidefinite.
Note: Problem is nonconvex as it is bilinear.
Expected behaviour: No crash in either case, report as nonconvex?
Should report 0 since feasible
z = sdpvar(2,1);
assign(z,[0;1]);
checkset(binary(z))
BarHomogeneous for instance is not available
yalmip('clear')
N = 5000;
w = sdpvar(N, 1);
previousPos = rand(N, 1);
constraint = [0.0001 <= abs(w) <= 0.04, sum(abs(w)) == 1, abs(sum(w)) <= 0.04, sum(abs(w - previousPos)) <= 0.1];
To reproduce
yalmip('clear')
sdpvar x y z
x = x^2;
y = y^2;
z = z^2;
p1 = (x+y)^4;
p2 = -8_x^2;
(x+y)_(x+y)
Fails
sdpvar x1 x2 u
plot([-1 <= x1 <= 1, x1+x2 == 1, 0 <= u <= 1])
Does not work if SOCP originates in a high-level model, since only the lifted constraint is marked as a cut. The internally generated SOCP will be a normal constraint
It would be uber-cool if YALMIP could plot non-convex sets. Admittedly, this is more a wish rather than a reasonable request.
sdpvar x1 x2
plot([ x1^2 + x2^2 >= 1 ; -2 <= [x1; x2] <= 2 ])
Should be easy to do, the basis is already in there. sin is one example
Reformulate to cos/sin
Add an example to show how to work with multipliers etc
Add a time tag when the internals are cleared and attach this to all objects created. If time tag on object is older than in core, it indicates yalmip('clear') has been issued since variable was created and user should be informed about the situation.
Support the format polytope(P,x)
Fails on everything
Variable bounds are extracted twice from model, e.g., in saveampl and compileinterfacedata
Add possibility to control options in snopt
Automatically cast SOCPs (which arise if you use norms) as quadratic constraints if the solver supports this.
Create some strcutured description of all non-external-solver specific options.
The 1-norm case should exploit symmetry when defining auxilliary variables and constraints
To reproduce
sdpvar x
hessian(x)
Should not work without binary variables, but it does...
sdpvar x
solvesdp([-1 <= x <= 1],x*abs(x))
Clean trailing zeros etc.
Models which instantiates to trivially infeasible SOCP cones are not handled correctly
yalmip('clear')
sdpvar x
sdpvar z
sdpvar y
P = optimizer([-1 <= y <= 1,y >= z*z],x^2,[],[y;z],x)
[~,err] = P{[0;2]};
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.