## Least-Squares (Model Fitting) Algorithms

### Least Squares Definition

Least squares, in general, is the problem of finding a vector x that is a local minimizer to a function that is a sum of squares, possibly subject to some constraints:

`$\underset{x}{\mathrm{min}}{‖F\left(x\right)‖}_{2}^{2}=\underset{x}{\mathrm{min}}\sum _{i}{F}_{i}^{2}\left(x\right)$`

such that A·x ≤ b, Aeq·x = beq, lb ≤ x ≤ ub.

There are several Optimization Toolbox™ solvers available for various types of F(x) and various types of constraints:

SolverF(x)Constraints
`mldivide`C·xdNone
`lsqnonneg`C·xdx ≥ 0
`lsqlin`C·xdBound, linear
`lsqnonlin`General F(x)Bound
`lsqcurvefit`F(x, xdata) – ydataBound

There are five least-squares algorithms in Optimization Toolbox solvers, in addition to the algorithms used in `mldivide`:

• `lsqlin` interior-point

• `lsqlin` active-set

• Trust-region-reflective (nonlinear or linear least-squares)

• Levenberg-Marquardt (nonlinear least-squares)

• The algorithm used by `lsqnonneg`

All the algorithms except `lsqlin` active-set are large-scale; see Large-Scale vs. Medium-Scale Algorithms. For a general survey of nonlinear least-squares methods, see Dennis [8]. Specific details on the Levenberg-Marquardt method can be found in Moré [28].

### Linear Least Squares: Interior-Point or Active-Set

The `lsqlin` `'interior-point'` algorithm uses the interior-point-convex quadprog Algorithm, and the `lsqlin` `'active-set'` algorithm uses the active-set `quadprog` algorithm. The `quadprog` problem definition is to minimize a quadratic function

`$\underset{x}{\mathrm{min}}\frac{1}{2}{x}^{T}Hx+{c}^{T}x$`

subject to linear constraints and bound constraints. The `lsqlin` function minimizes the squared 2-norm of the vector Cx – d subject to linear constraints and bound constraints. In other words, `lsqlin` minimizes

`$\begin{array}{c}{‖Cx-d‖}_{2}^{2}={\left(Cx-d\right)}^{T}\left(Cx-d\right)\\ =\left({x}^{T}{C}^{T}-{d}^{T}\right)\left(Cx-d\right)\\ =\left({x}^{T}{C}^{T}Cx\right)-\left({x}^{T}{C}^{T}d-{d}^{T}Cx\right)+{d}^{T}d\\ =\frac{1}{2}{x}^{T}\left(2{C}^{T}C\right)x+{\left(-2{C}^{T}d\right)}^{T}x+{d}^{T}d.\end{array}$`

This fits into the `quadprog` framework by setting the H matrix to 2CTC and the c vector to (–2CTd). (The additive term dTd has no effect on the location of the minimum.) After this reformulation of the `lsqlin` problem, `quadprog` calculates the solution.

Note

The `quadprog` `'interior-point-convex'` algorithm has two code paths. It takes one when the Hessian matrix H is an ordinary (full) matrix of doubles, and it takes the other when H is a sparse matrix. For details of the sparse data type, see Sparse Matrices. Generally, the algorithm is faster for large problems that have relatively few nonzero terms when you specify H as `sparse`. Similarly, the algorithm is faster for small or relatively dense problems when you specify H as `full`.

### Trust-Region-Reflective Least Squares

#### Trust-Region-Reflective Least Squares Algorithm

Many of the methods used in Optimization Toolbox solvers are based on trust regions, a simple yet powerful concept in optimization.

To understand the trust-region approach to optimization, consider the unconstrained minimization problem, minimize f(x), where the function takes vector arguments and returns scalars. Suppose you are at a point x in n-space and you want to improve, i.e., move to a point with a lower function value. The basic idea is to approximate f with a simpler function q, which reasonably reflects the behavior of function f in a neighborhood N around the point x. This neighborhood is the trust region. A trial step s is computed by minimizing (or approximately minimizing) over N. This is the trust-region subproblem,

 (1)

The current point is updated to be x + s if f(x + s) < f(x); otherwise, the current point remains unchanged and N, the region of trust, is shrunk and the trial step computation is repeated.

The key questions in defining a specific trust-region approach to minimizing f(x) are how to choose and compute the approximation q (defined at the current point x), how to choose and modify the trust region N, and how accurately to solve the trust-region subproblem. This section focuses on the unconstrained problem. Later sections discuss additional complications due to the presence of constraints on the variables.

In the standard trust-region method ([48]), the quadratic approximation q is defined by the first two terms of the Taylor approximation to F at x; the neighborhood N is usually spherical or ellipsoidal in shape. Mathematically the trust-region subproblem is typically stated

 (2)

where g is the gradient of f at the current point x, H is the Hessian matrix (the symmetric matrix of second derivatives), D is a diagonal scaling matrix, Δ is a positive scalar, and ∥ . ∥ is the 2-norm. Good algorithms exist for solving Equation 2 (see [48]); such algorithms typically involve the computation of all eigenvalues of H and a Newton process applied to the secular equation

`$\frac{1}{\Delta }-\frac{1}{‖s‖}=0.$`

Such algorithms provide an accurate solution to Equation 2. However, they require time proportional to several factorizations of H. Therefore, for trust-region problems a different approach is needed. Several approximation and heuristic strategies, based on Equation 2, have been proposed in the literature ([42] and [50]). The approximation approach followed in Optimization Toolbox solvers is to restrict the trust-region subproblem to a two-dimensional subspace S ([39] and [42]). Once the subspace S has been computed, the work to solve Equation 2 is trivial even if full eigenvalue/eigenvector information is needed (since in the subspace, the problem is only two-dimensional). The dominant work has now shifted to the determination of the subspace.

The two-dimensional subspace S is determined with the aid of a preconditioned conjugate gradient process described below. The solver defines S as the linear space spanned by s1 and s2, where s1 is in the direction of the gradient g, and s2 is either an approximate Newton direction, i.e., a solution to

 $H\cdot {s}_{2}=-g,$ (3)

or a direction of negative curvature,

 ${s}_{2}^{T}\cdot H\cdot {s}_{2}<0.$ (4)

The philosophy behind this choice of S is to force global convergence (via the steepest descent direction or negative curvature direction) and achieve fast local convergence (via the Newton step, when it exists).

A sketch of unconstrained minimization using trust-region ideas is now easy to give:

1. Formulate the two-dimensional trust-region subproblem.

2. Solve Equation 2 to determine the trial step s.

3. If f(x + s) < f(x), then x = x + s.

These four steps are repeated until convergence. The trust-region dimension Δ is adjusted according to standard rules. In particular, it is decreased if the trial step is not accepted, i.e., f(x + s) ≥ f(x). See [46] and [49] for a discussion of this aspect.

Optimization Toolbox solvers treat a few important special cases of f with specialized functions: nonlinear least-squares, quadratic functions, and linear least-squares. However, the underlying algorithmic ideas are the same as for the general case. These special cases are discussed in later sections.

#### Large Scale Nonlinear Least Squares

An important special case for f(x) is the nonlinear least-squares problem

 $\underset{x}{\mathrm{min}}\sum _{i}{f}_{i}^{2}\left(x\right)=\underset{x}{\mathrm{min}}{‖F\left(x\right)‖}_{2}^{2},$ (5)

where F(x) is a vector-valued function with component i of F(x) equal to fi(x). The basic method used to solve this problem is the same as in the general case described in Trust-Region Methods for Nonlinear Minimization. However, the structure of the nonlinear least-squares problem is exploited to enhance efficiency. In particular, an approximate Gauss-Newton direction, i.e., a solution s to

 $\mathrm{min}{‖Js+F‖}_{2}^{2},$ (6)

(where J is the Jacobian of F(x)) is used to help define the two-dimensional subspace S. Second derivatives of the component function fi(x) are not used.

In each iteration the method of preconditioned conjugate gradients is used to approximately solve the normal equations, i.e.,

`${J}^{T}Js=-{J}^{T}F,$`

although the normal equations are not explicitly formed.

#### Large Scale Linear Least Squares

In this case the function f(x) to be solved is

`$f\left(x\right)={‖Cx+d‖}_{2}^{2},$`

possibly subject to linear constraints. The algorithm generates strictly feasible iterates converging, in the limit, to a local solution. Each iteration involves the approximate solution of a large linear system (of order n, where n is the length of x). The iteration matrices have the structure of the matrix C. In particular, the method of preconditioned conjugate gradients is used to approximately solve the normal equations, i.e.,

`${C}^{T}Cx=-{C}^{T}d,$`

although the normal equations are not explicitly formed.

The subspace trust-region method is used to determine a search direction. However, instead of restricting the step to (possibly) one reflection step, as in the nonlinear minimization case, a piecewise reflective line search is conducted at each iteration, as in the quadratic case. See [45] for details of the line search. Ultimately, the linear systems represent a Newton approach capturing the first-order optimality conditions at the solution, resulting in strong local convergence rates.

Jacobian Multiply Function.  `lsqlin` can solve the linearly-constrained least-squares problem without using the matrix C explicitly. Instead, it uses a Jacobian multiply function `jmfun`,

`W = jmfun(Jinfo,Y,flag)`

that you provide. The function must calculate the following products for a matrix Y:

• If `flag == 0` then `W = C'*(C*Y)`.

• If `flag > 0` then `W = C*Y`.

• If `flag < 0` then ```W = C'*Y```.

This can be useful if C is large, but contains enough structure that you can write `jmfun` without forming C explicitly. For an example, see Jacobian Multiply Function with Linear Least Squares.

### Levenberg-Marquardt Method

The least-squares problem minimizes a function f(x) that is a sum of squares.

 $\underset{x}{\mathrm{min}}f\left(x\right)={‖F\left(x\right)‖}_{2}^{2}=\sum _{i}{F}_{i}^{2}\left(x\right).$ (7)

Problems of this type occur in a large number of practical applications, especially those that involve fitting model functions to data, such as nonlinear parameter estimation. This problem type also appears in control systems, where the objective is for the output y(x,t) to follow a continuous model trajectory φ(t) for vector x and scalar t. This problem can be expressed as

 $\underset{x\in {\Re }^{n}}{\mathrm{min}}\underset{{t}_{1}}{\overset{{t}_{2}}{\int }}{\left(y\left(x,t\right)-\phi \left(t\right)\right)}^{2}dt,$ (8)

where y(x,t) and φ(t) are scalar functions.

Discretize the integral to obtain an approximation

 $\underset{x\in {\Re }^{n}}{\mathrm{min}}f\left(x\right)=\sum _{i=1}^{m}{\left(y\left(x,{t}_{i}\right)-\phi \left({t}_{i}\right)\right)}^{2},$ (9)

where the ti are evenly spaced. In this problem, the vector F(x) is

`$F\left(x\right)=\left[\begin{array}{c}y\left(x,{t}_{1}\right)-\phi \left({t}_{1}\right)\\ y\left(x,{t}_{2}\right)-\phi \left({t}_{2}\right)\\ ...\\ y\left(x,{t}_{m}\right)-\phi \left({t}_{m}\right)\end{array}\right].$`

In this type of problem, the residual F(x)∥ is likely to be small at the optimum, because the general practice is to set realistically achievable target trajectories. Although you can minimize the function in Equation 7 using a general, unconstrained minimization technique, as described in Basics of Unconstrained Optimization, certain characteristics of the problem can often be exploited to improve the iterative efficiency of the solution procedure. The gradient and Hessian matrix of Equation 7 have a special structure.

Denoting the m-by-n Jacobian matrix of F(x) as J(x), gradient vector of f(x) as G(x), Hessian matrix of f(x) as H(x), and Hessian matrix of each Fi(x) as Di(x),

 $\begin{array}{l}G\left(x\right)=2J{\left(}^{x}F\left(x\right)\\ H\left(x\right)=2J{\left(}^{x}J\left(x\right)+2Q\left(x\right),\end{array}$ (10)

where

`$Q\left(x\right)=\sum _{i=1}^{m}{F}_{i}\left(x\right)\cdot {D}_{i}\left(x\right).$`

A property of the matrix Q(x) is that when the residual F(x)∥ tends towards zero as xk approaches the solution, then Q(x) also tends towards zero. So, when F(x)∥ is small at the solution, an effective method is to use the Gauss-Newton direction as a basis for an optimization procedure.

At each major iteration k, the Gauss-Newton method obtains a search direction dk that is a solution of the linear least-squares problem

 $\underset{{d}_{k}\in {\Re }^{n}}{\mathrm{min}}{‖J\left({x}_{k}\right){d}_{k}+F\left({x}_{k}\right)‖}_{2}^{2}.$ (11)

The direction derived from this method is equivalent to the Newton direction when the terms of Q(x) = 0. The algorithm can use the search direction dk as part of a line search strategy to ensure that the function f(x) decreases at each iteration.

The Gauss-Newton method often encounters problems when the second-order term Q(x) is nonnegligible. The Levenberg-Marquardt method overcomes this problem.

The Levenberg-Marquardt method (see [25] and [27]) uses a search direction that is a solution of the linear set of equations

 $\left(J{\left({x}_{k}\right)}^{T}J\left({x}_{k}\right)+{\lambda }_{k}I\right){d}_{k}=-J{\left({x}_{k}\right)}^{T}F\left({x}_{k}\right),$ (12)

or, optionally, of the equations

 $\left(J{\left({x}_{k}\right)}^{T}J\left({x}_{k}\right)+{\lambda }_{k}\text{diag}\left(J{\left({x}_{k}\right)}^{T}J\left({x}_{k}\right)\right)\right){d}_{k}=-J{\left({x}_{k}\right)}^{T}F\left({x}_{k}\right),$ (13)

where the scalar λk controls both the magnitude and direction of dk, and `diag(A)` means the matrix of diagonal terms in `A`. Set the option `ScaleProblem` to `'none'` to choose Equation 12, or set `ScaleProblem` to `'Jacobian'` to choose Equation 13.

You set the initial value of the parameter λ0 using the `InitDamping` option. Occasionally, the `0.01` default value of this option can be unsuitable. If you find that the Levenberg-Marquardt algorithm makes little initial progress, try setting `InitDamping` to a different value from the default, such as `1e2`.

When λk is zero, the direction dk is identical to that of the Gauss-Newton method. As λk tends towards infinity, dk tends towards the steepest descent direction, with magnitude tending towards zero. Consequently, for some sufficiently large λk, the term F(xk + dk) <  F(xk) holds true. Therefore, you can control the term λk to ensure descent even when the algorithm encounters second-order terms, which restrict the efficiency of the Gauss-Newton method. When the step is successful (gives a lower function value), the algorithm sets λk+1 = λk/10. When the step is unsuccessful, the algorithm sets λk+1 = λk*10.

Internally, the Levenberg-Marquardt algorithm uses an optimality tolerance (stopping criterion) of `1e-4` times the function tolerance.

The Levenberg-Marquardt method, therefore, uses a search direction that is a cross between the Gauss-Newton direction and the steepest descent direction.

Another advantage to the Levenberg-Marquardt method is when the Jacobian J is rank-deficient. In this case, the Gauss-Newton method can have numerical issues because the minimization problem in Equation 11 is ill-posed. In contrast, the Levenberg-Marquardt method has full rank at each iteration, and, therefore, avoids these issues.

The following figure shows the iterations of the Levenberg-Marquardt method when minimizing Rosenbrock's function, a notoriously difficult minimization problem that is in least-squares form.

Levenberg-Marquardt Method on Rosenbrock's Function

For a more complete description of this figure, including scripts that generate the iterative points, see Banana Function Minimization.

#### Bound Constraints in Levenberg-Marquardt Method

When the problem contains bound constraints, `lsqcurvefit` and `lsqnonlin` modify the Levenberg-Marquardt iterations. If a proposed iterative point x lies outside of the bounds, the algorithm projects the step onto the nearest feasible point. In other words, with P defined as the projection operator that projects infeasible points onto the feasible region, the algorithm modifies the proposed point x to P(x). By definition, the projection operator P operates on each component xi independently according to

or, equivalently,

`$P\left({x}_{i}\right)=\mathrm{min}\left(\mathrm{max}\left({x}_{i},{\text{lb}}_{i}\right),{\text{ub}}_{i}\right).$`

The algorithm modifies the stopping criterion for the first-order optimality measure. Let x be a proposed iterative point. In the unconstrained case, the stopping criterion is

 ${‖\nabla f\left(x\right)‖}_{\infty }\le \text{tol,}$ (14)

where tol is the optimality tolerance value, which is `1e-4*FunctionTolerance`. In the bounded case, the stopping criterion is

 ${‖x-P\left(x-\nabla f\left(x\right)\right)‖}_{\infty }^{2}\le \text{tol}{‖\nabla f\left(x\right)‖}_{\infty }.$ (15)

To understand this criterion, first note that if x is in the interior of the feasible region, then the operator P has no effect. So, the stopping criterion becomes

`${‖x-P\left(x-\nabla f\left(x\right)\right)‖}_{\infty }^{2}={‖\nabla f\left(x\right)‖}_{\infty }^{2}\le \text{tol}{‖\nabla f\left(x\right)‖}_{\infty },$`

which is the same as the original unconstrained stopping criterion, ${‖\nabla f\left(x\right)‖}_{\infty }\le \text{tol}$. If the boundary constraint is active, meaning x – ∇f(x) is not feasible, then at a point where the algorithm should stop, the gradient at a point on the boundary is perpendicular to the boundary. Therefore, the point x is equal to P(x – ∇f(x)), the projection of the steepest descent step, as shown in the following figure.

Levenberg-Marquardt Stopping Condition