Thanks for your answer.

I do understand that this would slow the script, however, when the solver is turned off it would not make it slow (because it is not making any calculations, correct?).

Unfortunately I am not able to share the script.

One thing, do you have autosave turned on and large amounts of internalised data in the script? This will result in GH writing the entire doc to the disk when you do anything in GH whether the solver is on or not.

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A lot of the short sudoku solvers just recursively try every possible legal number left until they have successfully filled the cells. I haven't taken this apart, but just skimming it, it looks like that's what it does.

Here is a little background information. The solver in Fusion is the Autodesk Nastran solver. Nastran started in the late 1960's, and Autodesk's version of Nastran started in the 1990's (as NEi Nastran). From this perspective, the solver is well established.

Adaptive solver for stiff systems of ODEs using semi-implicit Runge-Kutta method of third order - GitHub - ivan-pi/stiff3: Adaptive solver for stiff systems of ODEs using semi-implicit Runge-Kutta ...

I do atmospheric chemical kinetics involving 10,000s ODEs, which are very stiff. For this problem, one of the best methods is CVODE BDF with the banded matrix solver. CVODE is written in C but has an excellent Fortran interface.

Nevertheless, I tackled my particular problem now the other way around. Instead of building the Hodrick-Prescott filter myself (with the help of the grg nonlinear solving method of the infamous excel solver), I looked for a ready-built package. Though I usually refrain from using scripting within KNIME, as I value the ease of low coding solutions, I found a python package, which provides the HP filter.

The solver option allows you to specify the solver method to use in GLM and GAM. When specifying a solver, the optimal solver depends on the data properties and prior information regarding the variables (if available). In general, the data are considered sparse if the ratio of zeros to non-zeros in the input matrix is greater than 10. The solution is sparse when only a subset of the original set of variables is intended to be kept in the model. In a dense solution, all predictors have non-zero coefficients in the final model.

A solver is a piece of mathematical software, possibly in the form of a stand-alone computer program or as a software library, that 'solves' a mathematical problem. A solver takes problem descriptions in some sort of generic form and calculates their solution. In a solver, the emphasis is on creating a program or library that can easily be applied to other problems of similar type.

The General Problem Solver (GPS) is a particular computer program created in 1957 by Herbert Simon, J. C. Shaw, and Allen Newell intended to work as a universal problem solver, that theoretically can be used to solve every possible problem that can be formalized in a symbolic system, given the right input configuration. It was the first computer program that separated its knowledge of problems (in the form of domain rules) from its strategy of how to solve problems (as a general search engine).

General solvers typically use an architecture similar to the GPS to decouple a problem's definition from the strategy used to solve it. The advantage in this decoupling is that the solver does not depend on the details of any particular problem instance. The strategy utilized by general solvers was based on a general algorithm (generally based on backtracking) with the only goal of completeness. This induces an exponential computational time that dramatically limits their usability. Modern solvers use a more specialized approach that takes advantage of the structure of the problems so that the solver spends as little time as possible backtracking.

"Love at first mask...Without a doubt in my mind, the problem solver is the best thing to grace my skin. The problem solver is magic, it is the healing hand of some higher power. Let me start off by saying this: my skin is oily and acne prone so when I came across the problem solver, my eyes just about fell out of my head reading the description. Is it too good to be true? UMM NO and here's why:

Obviously, you don't see results right away but your skin does feel different, and in that moment you will know that there is a possibility of love at first use :) Right away, skin feels like a delicate flower petal, smooth and fresh. I notice the change in my skin every time I use the problem solver more frequently, no if's, and's, or but's for this miracle worker. It instantly clears blemishes the next day which makes it a great go-to for some emergency skin care for an event or occasion. If your skin is in need of a tune-up the problem solver is your new go-to. So what are you waiting for??"

Loving the speed of the new sparse solver in Houdini 18. I have a job where I have a medium res simulation and I need to upres it for final render without the motion changing to much. I would usually use the Up-Res shelf tool to set this up, but have found it doesn't accept the sparse solver as a source. Is it possible to manually set these up, has anyone tried it?

The sparse solver itself is a deeply modified sparse pyro where you basicly cancel all nodes relatives to vel advection / projection and import the vel from the lowres in copy mode. Then you can add your sources (without the vel or v sourcing since it has already been calculated in the lowres) and add some noise. Since vel is in copy your noise won't last in the sim so i created a noise channel in the smoke object that is advected by the vel and merge to the vel.

Your new simulation should have smaller voxels and parameters close to the lowres (like temperature diffusion, flame amount, time step) if you want this new version to be close to the lowres. You then copy the lowres velocity and simply advect the upres by it. This way you only perform heavy computation like divergent free velocity (ensure the conservation of the mass of the fluid) and the boundary collision on the lowres, the upres solver only has an advect for the density / temperature / ect. all other nodes relative to velocity enforcement / computation are deseabled which allow you to quiclky compute the upres and skip all the heavy parts.

I just tried following this tutorial. So I was using the two versions of mark which were included with the software, one with the animation and one with the blend shapes and when i tried to set up the blendShape solver i got this message:

When laziness is true, the constraint is only considered by the LinearProgramming solver if its current solution violates the constraint. In thiscase, the constraint is definitively added to the problem. This may beuseful in some MIP problems, and may have a dramatic impact on performance.

SAT based solver (requires only integer and Boolean variables). If you pass it mixed integer problems, it will scale coefficients to integer values, and solve continuous variables as integral variables.

Returns a non-OK status if a problem arised (typically, if it wasn't usedlike it should be):- loading a solution whose variables don't correspond to the solver'scurrent variables- loading a solution with a status other than OPTIMAL / FEASIBLE.

Some solvers (MIP only, not LP) can produce multiple solutions to theproblem. Returns true when another solution is available, and updates theMPVariable* objects to make the new solution queryable. Call only aftercalling solve.

As of 2020-02-10, only Gurobi and SCIP support NextSolution(), seelinear_solver_interfaces_test for an example of how to configure thesesolvers for multiple solutions. Other solvers return false unconditionally.

Set a hint for solution. If a feasible or almost-feasible solution to the problem is already known, it may be helpful to pass it to the solver so that it can be used. A solver that supports this feature will try to use this information to create its initial feasible solution. Note that it may not always be faster to give a hint like this to the solver. There is also no guarantee that the solver will use this hint or try to return a solution "close" to this assignment in case of multiple optimal solutions.

If the variable is integer, then the value will always be an integer (theunderlying solver handles floating-point values only, but this functionautomatically rounds it to the nearest integer; see: man 3 round).

What can go wrong when using preconditoned Krylov methods from KSP (PETSc's linear solver package) to solve a sparse linear system such as those obtained by discretizing and linearizing partial differential equations? 0852c4b9a8

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