I have been using a previous version of YALMIP, but after installing the new version nothing works.

Restart MATLAB.

Q:

..still doesn't work!

A:

Remove any old version of YALMIP before you install the new version. Do not just copy the new version into the old YALMIP directory.

Q:

..still doesn't work!

A:

Added all the paths? (/yalmip, /yalmip/extras, /yalmip/demos, /yalmip/solvers)

Q:

..still doesn't work!

A:

Did your unzip application really decompress the files to the path structure, instead of placing all files in one directory.

Q:

..still doesn't work!

A:

Are you sure you added all the paths! (/yalmip, /yalmip/extras, /yalmip/demos, /yalmip/solvers)

Q:

..still doesn't work!

A:

Could be related to toolbox path caching (open menu File/Preferences/General and run "Update Toolbox Path Cache")

Q:

..still doesn't work!

A:

Removed your old YALMIP version from the MATLAB path?

Q:

..still doesn't work!

A:

Added all the paths to your solver?

Q:

...still doesn't work!

A:

Do you have any solver installed?

Q:

...still doesn't work!

A:

Compiled the solver (if needed)? Compiled it for the correct MATLAB version?

Q:

...still doesn't work!

A:

Using MATLAB 5.3.1 or later?

Q:

...still doesn't work!

A:

Probably a PICNIC problem ;-P

Q:

It is so slow!

A:

As a rule of thumb, the time reported as 'yalmiptime' in the output diagnostic should be around fractions of a second for small problems, and typically a fraction of the actual solution time for larger problems. If this is not the case, you probably have a problem with your installation.

As another rule of thumb, yalmiptest(sdpsettings('verbose',0,'solver','sedumi')) takes 2-5 seconds (including initial search for solvers) on my PC machines (2GHz and 3GHz).

Do you have FEMLAB on your MATLAB path? Removing FEMLAB from the path can improve performance significantly. Another way to make YALMIP faster is to set the field cachesolvers in sdpsettings to 1. (One reason for the extremely poor performance of YALMIP in these cases is typically due to slow network functionalities, making the command exist , which is used in solvesdp when detecting solvers, very slow.

Are you running over a network? This can be extremely detrimental for general performance in YALMIP. On some networked Linux version, YALMIP runs 10-100 times slower!

Do you have a poor sparse matrix library in your installation? Some distributions seem to have extremely poor performance for sparse matrices (observed on Linux), which is used a lot in YALMIP.  One way to test this is to run the script below. If the displayed result is substantially larger than 20, you have problems (and I have no idea how you can resolve this...)

d = sprandn(1e5,1,0.5);
tic;for i= 1:1000;d([2000:3000]);end;t1 = toc;
d = d';
tic;for i= 1:1000;d([2000:3000]);end;t2 = toc;t1/t2

Do you have MOSEK installed? This solver overloads the function optimset in Mathworks optimization toolbox, but is much slower. optimset is called in sdpsettings to initialize the option fields for LINPROG and QUADPROG. Note that sdpsettings is called in solvesdp if no options structure is passed in the call. Hence, for optimal speed, define the options structure once, and always use three arguments when calling solvesdp. This is recommended even if you do not have MOSEK.

Are you working with nonlinear expressions? In that case, clear the internals of YALMIP regularly using yalmip('clear').

Are you working with large polynomial expressions? Try to compile the file findhash.c (mex findhash.c)

Have you formulated a standard primal SDP form problem and expected YALMIP to solve it that way? Make sure to read the section on dualization.

Finally, standard MATLAB coding practice applies. This means that you should try to vectorize code, define things once etc.

Q:

YALMIP cannot find any solver.

A:

Do you have any solver? You typically need to install a (or several) solver and update you path. See interfaced solver.

Q:

PENBMI does not work with YALMIP anymore.

A:

Version 1.1 and earlier will not work directly anymore. However, this is easily fixed. Edit the file callpenbmim.m (if you use the PENOPT version) or callpenbmi.m (if you use the TOMLAB version). Uncomment the code below the comment "UNCOMMENT THIS".

Q:

SDPT3-3.02 does not work with YALMIP anymore.

A:

Download SDPT3-3.02 and compile (code updated without version increment)

Q:

My version of SDPT3-3.02 does not work and complains about the file svec.

A:

Do you have the solver SDPPACK installed? Remove the path to this solver.

Q:

SDPT3 (or SeDuMi) does not work.

A:

Make sure not to have both SDPT3 (version 3.02) and SeDuMi on the MATLAB path. This problem is resolved in SDPT3 3.1

Q:

CSDP does not work.

A:

Do you have CSDP in your system path?

Q:

CSDP runs but crashes.

A:

Running MATLAB 6.1 and CSDP 4.6? In that case, edit readsol.m in the CSDP directory and replace all occurrences of && with &. Even better, download the latest version of CSDP.

Q:

I already have LMILAB and LINPROG installed, do I need any other solver?

A:

Yes, at-least if you intend to solve anything but a few small problems.  See next issue.

Q:

LMILAB is slower when I use it with YALMIP

A:

Yes. YALMIP, and all other supported solvers, works with a completely general SDP formulation in contrast to LMILAB that requires the problem structure to be explicitely described by the user for speed (in particular for control related problems). Rule of thumb : Do not use LMILAB with YALMIP. If you have made the effort to download YALMIP, take 5 more minutes and install a more efficient and general solver. Future versions of YALMIP may resolve this issue. Constraints defined using the KYP operator is efficiently handled in some cases already.

Q:

CDD hangs

A:

Try sdpsettings('cdd.method','dual-simplex')

Q:

fmincon crashes

A:

Do you have MOSEK installed? This can cause problems due to an inconsistency between MATLABs and MOSEKs implementation of the file optimget.m. Remove MOSEK from your path.

Q:

MAXDET fails

A:

MAXDET seems to be sensitive to unbounded feasible regions and unconstrained variables. Try to add redundant bound constraints on all your variables.

Q:

XPRESS performs badly, claims infeasibility etc.

A:

Adding bounds on the involved variables solves this issue in many cases.

Q:

There are so many solvers, which one should I use?

A:

SeDuMi and SDPT3 are good general purpose SDP solvers (not necessarily the best solvers though), efficient also on LP and SOCP problems and reasonably efficient on small QP problems. Hans D. Mittelmanns benchmark might be helpful. If you mainly solve LPs, make sure to try the free solvers GLPK, QSOPT, CLP and CDD

Q:

The solution I get in an SDP is not feasible but has eigenvalues around, say, -1e-6.

A:

Most solvers actually use infeasible/exterior algorithms, so slightly infeasible solutions are common.

Q:

Can I solve BMIs without PENBMI?

A:

The BMI-examples in yalmipdemo show some alternative ways to code your own solver rather easily, but for performance and robustness, PENBMI is highly recommended.

Q:

My solution is not what I expected.

A:

Is your problem what you expected? Use the command checkset to see that you actually have the constraints that you meant to declare (does it say matrix inequality or element-wise inequality etc).

Are your variables really what you meant to declare (display them to see if they are symmetric, Hermitian, full, etc.).

Most common error is that you have declared a square matrix but accidentally forgotten to declare it as full and obtained a symmetric matrix instead (square matrices are full by default!)

Q:

Typing help set gives me no information on the YALMIP function set.

A:

Yep, a bit tricky since set also is a built-in function. Type help sdpvar/set and you will find what you are looking for.

Q:

Is there really a set class?

A:

No...set is only a wrapper to call the old class lmi. To much work to re-write all code just for a name change.

Q:

Is set related to the "normal" set command in MATLAB

A:

No... The name set was selected since it is short. An alternative would be to define constraints, or feasible sets, using a command named, e.g., constraint. However, my keyboard typing speed is too slow to allow for such a long command name. In my opinion, the name set is at-least better than the old name lmi.

Q:

I define a semidefinite constraint, but YALMIP declares it "element-wise".

A:

YALMIP detects semidefinite constraints by checking symmetry. In some cases (working with very ill-conditioned data), numerical problems may lead to a small violation of symmetry in MATLAB, and YALMIP will declare the constraint as element-wise. To solve this problem, just symmetrize your variable first.

Q:

Are inequalities really strict?

A:

By default, strict (<,>) and non-strict (<=,>=) inequalities are treated in the same way in YALMIP, and the result depends on the solver. However, by using the field shift in sdpsetttings, inequalities defined using < and > will be treated slightly different. YALMIP will add a small perturbation to these inequalities to increase the likelihood of a strictly feasible solution. Warning : If you have an integer variable, and add a constraint set(x<2), this will not be interpreted as set(x<=1) . To avoid confusion, use set(x<=1). The same holds for rank constraints set(rank(x)<2).

Q:

How do I solve generalized eigenvalue problems (like gevp in LMILAB)?

A:

Two options. The first one is to code your own script based on a simple bisection. This is illustrated in the example decayex.m. An alternative is to install the BMI solver PENBMI. This solver solves GEVP problems globally.

Q:

I get strange results when I use the option 'removeequalities'

A:

When equality constraints are removed by YALMIP by deriving a reduced basis ('removeequalities' set to 1 or 2) dual variables will not be recovered. This may lead to further complications in some cases. If you are solving a problem that you have derived by using the function dualize, you original variables will not be computed, since they are computed from the missing duals. Another case is when you solve a linearly parameterized sum of squares problem using a kernel model ('sos.model' set to 0 or 1). The parameterization variables are computed from the dual variables, hence failure will occur. To summarize, do not use the option 'removeequalities' in sum-of-squares problems or dualized problems, unless you really now what you are doing.

Q:

YALMIP complains about failing in convexity propagation

A:

This means that you have used so called nonlinear operators to model your problem, and most likely you have defined a problem which cannot be represented using standard convex constraints. If you know that your model is convex, try to model the nonlinear terms by hand to see if you actually are correct (the convexity analysis is conservative, although in most cases failure in the convexity propagation is due to actual nonconvexity.

Q:

I have problems solving a geometric program.

A:

To begin with, you need a solver that can solve geometric programs (YALMIP currently supports GPPOSY, Mosek and fmincon). If you still have problems, the reason may be that YALMIP converts convex quadratic constraints to seconds order constraints. This should not be done in geometric programs (it is a known bug). To avoid this, set the option 'convertconvexquad' to 0. Another reason is that runs into problem during the expansion of nonlinear operators. To avoid this issue, explicitly tell YALMIP that the problem is a geometric problem by specifying the solver to 'gpposy', 'mosek-geometric' or 'fmincon-geometric'.

Q:

I can not write X = eye(2); X(1,1)=sdpvar(1,1)!

A:

This is because of the way the object orientation works in MATLAB. There is currently no way to support this feature (without overloading the @double class, which can cause instability.)

Q:

Vector-valued quadratic constraints

A:

If you have vector-valued quadratic constraints with mixed convexity and concavity, things may fail. To avoid problems, use the options sdpsettings('convertconvexquad',0), or simply do not use a vectorized constraint. Turning off the automatic conversion of convex quadratic constraints to second order cones is typically recommended if you know that you will not solve the problem using a semidefinite solver.

Q:

Using implies on set objects

A:

Using the logical constraint implies with the first argument being a constraint is currently causing problems in some cases, and should thus be avoided.

Q:

Sometimes MATLAB fails to declare an SDPVAR object named i or j inside a function

A:

This is one of the weirdest bugs in MATLAB I have come across. If you define your own class (such as SDPVAR) and use the constructor inside of a function body and assign the name i or j to the object, MATLAB fails to perform the definition, and i or j remains to point to the imaginary number. The bug has been reported and been acknowledged by The Mathworks. If you stumble upon this problem (very rare), just change the variable name.

Q:

I have found a bug. What to do?

A:

Send a mail to loefberg@control.ee.ethz.ch. Type ver in MATLAB and include the result in the mail. If possible, include the code (as simple as possible) that generated the fault, otherwise, include at-least the error message etc. The more information the better.

Q:

I have an idea for a new feature. What to do?

A:

Send a mail to loefberg@control.ee.ethz.ch. However, I only add new features when I need them my self, but if your idea is good enough, I might realize that I need it!

Q:

What does YALMIP mean?

A:

Used to be short for Yet Another LMI Parser. However, since YALMIP is much more than a LMI parser now, it does not mean anything.