This example shows how to determine the shape of a circus tent by solving a quadratic optimization problem. The tent is formed from heavy, elastic material, and settles into a shape that has minimum potential energy subject to constraints. A discretization of the problem leads to a bound-constrained quadratic programming problem.

For a problem-based version of this example, see Bound-Constrained Quadratic Programming, Problem-Based.

Consider building a circus tent to cover a square lot. The tent has five poles covered with a heavy, elastic material. The problem is to find the natural shape of the tent. Model the shape as the height *x*(*p*) of the tent at position *p*.

The potential energy of heavy material lifted to height *x* is *cx*, for a constant *c* that is proportional to the weight of the material. For this problem, choose *c* = 1/3000.

The elastic potential energy of a piece of the material $${E}_{stretch}$$ is approximately proportional to the second derivative of the material height, times the height. You can approximate the second derivative by the 5-point finite difference approximation (assume that the finite difference steps are of size 1). Let $$\Delta x$$ represent a shift of 1 in the first coordinate direction, and $$\Delta y$$ represent a shift by 1 in the second coordinate direction.

$${E}_{stretch}(p)=(-1(x(p+{\Delta}_{x})+x(p-{\Delta}_{x})+x(p+{\Delta}_{y})+x(p-{\Delta}_{y}))+4x(p))x(p).$$

The natural shape of the tent minimizes the total potential energy. By discretizing the problem, you find that the total potential energy to minimize is the sum over all positions *p* of $${E}_{stretch}(p)$$ + *cx*(*p*).

This potential energy is a quadratic expression in the variable `x`

.

Specify the boundary condition that the height of the tent at the edges is zero. The tent poles have a cross section of 1-by-1 unit, and the tent has a total size of 33-by-33 units. Specify the height and location of each pole. Plot the square lot region and tent poles.

height = zeros(33); height(6:7,6:7) = 0.3; height(26:27,26:27) = 0.3; height(6:7,26:27) = 0.3; height(26:27,6:7) = 0.3; height(16:17,16:17) = 0.5; colormap(gray); surfl(height) axis tight view([-20,30]); title('Tent Poles and Region to Cover')

The `height`

matrix defines the lower bounds on the solution `x`

. To restrict the solution to be zero at the boundary, set the upper bound `ub`

to be zero on the boundary.

```
boundary = false(size(height));
boundary([1,33],:) = true;
boundary(:,[1,33]) = true;
ub = inf(size(boundary)); % No upper bound on most of the region
ub(boundary) = 0;
```

The `quadprog`

problem formulation is to minimize

$$\frac{1}{2}{x}^{T}Hx+{f}^{T}x$$.

In this case, the linear term $${f}^{T}x$$ corresponds to the potential energy of the material height. Therefore, specify *f* = 1/3000 for each component of *x*.

f = ones(size(height))/3000;

Create the finite difference matrix representing $${E}_{stretch}$$ by using the `delsq`

function. The `delsq`

function returns a sparse matrix with entries of 4 and -1 corresponding to the entries of 4 and -1 in the formula for $${E}_{stretch}(p)$$. Multiply the returned matrix by 2 to have `quadprog`

solve the quadratic program with the energy function as given by $${E}_{stretch}$$.

`H = delsq(numgrid('S',33+2))*2;`

View the structure of the matrix `H`

. The matrix operates on `x(:)`

, which means the matrix `x`

is converted to a vector by linear indexing.

```
spy(H);
title('Sparsity Structure of Hessian Matrix');
```

Solve the problem by calling `quadprog`

.

x = quadprog(H,f,[],[],[],[],height,ub);

Minimum found that satisfies the constraints. Optimization completed because the objective function is non-decreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance.

Reshape the solution `x`

to a matrix `S`

. Then plot the solution.

```
S = reshape(x,size(height));
surfl(S);
axis tight;
view([-20,30]);
```