I am currently doing research on the calibration of the robots' geometry, which is a standard and well-studied topic. In fact, it can be formulated as a nonlinear non-convex optimization problem:

Imagine the robot position is denoted by $X(\theta)$, where $\theta$ is the geometry parameter that is erroneous. $X(\theta)$ is a nonlinear function. The objective function in this case is:

$$ \min_\theta \sum_{k = 1}^N \| X_k-X_k(\theta) \|_2 = \min_\theta \sum_{k = 1}^N \| \Delta r_k \|_2, $$ with $N$ being the number of observations and $X_k$ the ground truth coming from accurate sensors. However, in the literature, almost everyone linearizes this problem via Taylor series without justifications. The linearized version is given by

$$ \min_{\Delta \theta} \sum_{k = 1}^N \frac{1}{2} \| \frac{\partial X_k(\theta)}{\partial \theta} \Delta \theta - \Delta r_k\|_2, $$ with $\theta_{true} = \theta_{nominal} + \Delta \theta$.

So, my questions are somewhat on the philosophical side.

  1. Why do we linearize nonlinear optimization problems in the first place? Is it to make the problem convex?
  2. Don't we sacrifice accuracy by linearizing the problem via the Taylor series?
  3. With the current advanced methods and powerful processors/solvers, do we still need to linearize these problems?

Thank you in advance!

P.S. This problem is typically solved offline, to calibrate the robots every 1 or 2 years in the industry. Also, the number of optimization parameters rarely exceeds 28 (4 number of the DH parameters * 7 degrees of freedom). So, time and computational power are of no concern for this particular problem.

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    $\begingroup$ Can you give concrete examples of what you mean? There are local approximations of the objective in order to study the local convergence properties of a particular method. Some applications also suffice having an approximate solution which is easier to compute, especially if it's a large-scale optimization problem. $\endgroup$
    – V.S.e.H.
    Aug 28, 2023 at 13:33
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    $\begingroup$ Sure, I have updated the original post with the example! $\endgroup$ Aug 28, 2023 at 14:05
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    $\begingroup$ Linearization techniques are quite common in robotics applications, especially like the one you provided, if the goal is to solve this problem on each new sample batch from the sensors. Most likely there are also (hard) real time constraints, then linearization greatly simplifies the problem, to the point of probably having deterministic execution time bounds. The EKF is a great example of this. $\endgroup$
    – V.S.e.H.
    Aug 28, 2023 at 14:16
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    $\begingroup$ The constraints are usually the upper/lower bounds of the parameters, which are set empirically. To my knowledge, there is no hard constraint. Additionally, this problem is solved offline, once for a long time. So you have all the time and computational power to solve it. $\endgroup$ Aug 28, 2023 at 14:37
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    $\begingroup$ Do they solve this just once, or is this just one step of sequential quadratic programming where they repeat a few times? I.e., linearize-solve-linearize-solve-linearize-… $\endgroup$
    – Nick Alger
    Aug 28, 2023 at 15:04

2 Answers 2


We can’t solve high degree equation exactly. And even if we could, it would be very slow. The first order (linear) approximation steps iterate the linear equations till the precision is good enough. This can be done much quicker that the higher degree equations, that have their own problems: they are slow, not always very stable and more difficult to implement. The first order is always the best way to start understand something, either in numerical calculations (where it is quicker, easier and more efficient) or in basic modelling of problems (where it is often necessary to get any results).


I will try to cover your three points by talking a little bit about optimization in general.

Large non-convex optimization problems have to be solved iteratively. When the functions are differentiable, the most common strategy is to use information from the gradient to update our variables in each iteration (order-$1$ methods). But we do have order-$0$ methods (in which we use only information about the function itself); and order-$2$ methods (in which the information from the second derivatives are also included).

The most common approach to non-convex optimization problems is to solve them through Sequential Convex Programming, or Sequential Linear Programming (which is a specific case of Sequential Convex Programming). These algorithms are quite efficient, because we have very good convex optimizers available in literature and in computational packages.

The main idea is of using a less accurate approximations is to speed up each iteration, and to achieve a sequence of ever improving candidates for the solution. In your case, it seems that you are not solving the original non-convex problem at all, you are solving only the linearized subproblem right? This can be a valid solution if you can guarantee that all values of $\Delta \theta$ are sufficiently small (you can check this after solving the linearized sub-problem).

In some cases, we may have a non-linear problem that is convex, but we may still want to linearize it because we can then use the linearity properties somehow, or because we could then use a linear programming solver.

There is no rule of thumb, these decisions are highly problem-dependent. All the following questions are relevant to define how to approach the optimization problem: How much time do we have to solve it? How many variables do we have? How expensive is to compute the original function? How expensive is to compute its gradient? How expensive is to compute or to estimate its second derivatives? Small variations can be assumed? What are my computational resources? Will we solve it analytically, with a CPU, with a GPU? An approximated result is enough for my application? How close my approximation is from the real solution?

  1. We linearize the problem when the gains from the linearization (very nice and useful properties, very quick to solve) compensate the accuracy that we lose. Even linearizing the problem, exact solutions can be obtained through iterative methods.

  2. If we solve a single linearized subproblem, the solution will be approximated, it is up to the human designer to evaluate if this is acceptable for the considered application.

  3. We don't have to use such approaches, but they can be used since they work very well. There are many methods, some perform linearizations, some generate non-linear convex subproblems, some are simple gradient descents, some are order-$0$ heuristic algorithms... But, in my opinion, linearization approaches will be never be put out of use since, whenever they can be used, they probably are the most efficient ones.

  • $\begingroup$ I am not sure if I totally understand what you mean by "function expression". But according to your explanation, can I say the function or at least the data points are not smooth enough? So we need to use the regression methods. $\endgroup$ Aug 29, 2023 at 13:05
  • $\begingroup$ I understood that you do not have a function like $f(x_1,x_2) = x_1 + {x_2}^3 + 5\,x_1\,x_2$, which you could differentiate and solve the optimization problem by many different ways (including Sequential Linear Programming). What you have is a set of points $\{(x_1,x_2)_a, (x_1,x_2)_b, ..., (x_1,x_2)_z\}$ and the corresponding function values (experimentally measured) $\{f_a,f_b,...,f_z\}$. Therefore, you need some kind of regression, some candidate function that will fit your data, so order-$1$ methods can be applied. Isn't that right? $\endgroup$ Aug 29, 2023 at 14:18
  • $\begingroup$ Sorry, I am a bit puzzled. We have a function $X(\theta)$ that maps the optimization variables $\theta$ to the measurement space. This function is fully known analytically, similar to the $f(x_1,x_2)$ you wrote. It is expected that with the correct $\theta_{true}$, the analytical solution $X(\theta_{true})$ will match the measurements $X$. The initial optimization that I provided in the original post should reflect this idea. $\endgroup$ Aug 29, 2023 at 14:48
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    $\begingroup$ My mistake then, I misunderstood. In this case, you should be able to obtain an exact optimum. The linear approximation could be used in a iterative solver to do so. If you are solving only one linearized subproblem... It would only make sense if the variations are known to be really very small, so that the accuracy lost is irrelevant. Because, under these settings (small number of variables, no limitation on computational resources or on execution time), it should be relatively easy to obtain exact optimum points (maybe even a global optimum). $\endgroup$ Aug 29, 2023 at 16:09

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