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Constrained Electrostatic Nonlinear Optimization, Problem-Based

Consider the electrostatics problem of placing 10 electrons in a conducting body. The electrons will arrange themselves in a way that minimizes their total potential energy, subject to the constraint of lying inside the body. All the electrons are on the boundary of the body at a minimum. The electrons are indistinguishable, so the problem has no unique minimum (permuting the electrons in one solution gives another valid solution). This example was inspired by Dolan, Moré, and Munson [1].

For an equivalent solver-based example using Symbolic Math Toolbox™, see Symbolic Math Toolbox Calculates Gradients and Hessians.

Problem Geometry

This example involves a conducting body defined by the following inequalities. For each electron with coordinates (x,y,z),

z-|x|-|y|x2+y2+(z+1)21.

These constraints form a body that looks like a pyramid on a sphere. To view the body, enter the following code.

[X,Y] = meshgrid(-1:.01:1);
Z1 = -abs(X) - abs(Y);
Z2 = -1 - sqrt(1 - X.^2 - Y.^2);
Z2 = real(Z2);
W1 = Z1; W2 = Z2;
W1(Z1 < Z2) = nan; % only plot points where Z1 > Z2
W2(Z1 < Z2) = nan; % only plot points where Z1 > Z2
hand = figure; % handle to the figure, since we'll plot more later
set(gcf,'Color','w') % white background
surf(X,Y,W1,'LineStyle','none');
hold on
surf(X,Y,W2,'LineStyle','none');
view(-44,18)

A slight gap exists between the upper and lower surfaces of the figure. This gap is an artifact of the general plotting routine used to create the figure. The routine erases any rectangular patch on one surface that touches the other surface.

Define Problem Variables

The problem has ten electrons. The constraints give bounds on each x and y value from –1 to 1, and the z value from –2 to 0. Define the variables for the problem.

N = 10;
x = optimvar('x',N,'LowerBound',-1,'UpperBound',1);
y = optimvar('y',N,'LowerBound',-1,'UpperBound',1);
z = optimvar('z',N,'LowerBound',-2,'UpperBound',0);
elecprob = optimproblem;

Define Constraints

The problem has two types of constraints. The first, a spherical constraint, is a simple polynomial inequality for each electron separately. Define this spherical constraint.

elecprob.Constraints.spherec = (x.^2 + y.^2 + (z+1).^2) <= 1;

The preceding constraint command creates a vector of ten constraints. View the constraint vector using showconstr.

showconstr(elecprob.Constraints.spherec)
  ((x.^2 + y.^2) + (z + 1).^2) <= arg_RHS

  where:

    arg2 = 1;
    arg1 = arg2([1 1 1 1 1 1 1 1 1 1]);
    arg_RHS = arg1(:);

The second type of constraint in the problem is linear. You can express the linear constraints in different ways. For example, you can use the abs function to represent an absolute value constraint. To express the constraints this way, write a MATLAB function and convert it to an expression using fcn2optimexpr. For a different approach, write the absolute value constraint as four linear inequalities. Each constraint command returns a vector of ten constraints.

elecprob.Constraints.plane1 = z <= -x-y;
elecprob.Constraints.plane2 = z <= -x+y;
elecprob.Constraints.plane3 = z <= x-y;
elecprob.Constraints.plane4 = z <= x+y;

Define Objective Function

The objective function is the potential energy of the system, which is a sum over each electron pair of the inverse of their distances:

energy=i<j1electron(i)-electron(j).

Define the objective function as an optimization expression.

energy = optimexpr(1);
for ii = 1:(N-1)
    for jj = (ii+1):N
        tempe = (x(ii) - x(jj))^2 + (y(ii) - y(jj))^2 + (z(ii) - z(jj))^2;
        energy = energy + tempe^(-1/2);
    end
end
elecprob.Objective = energy;

Run Optimization

Start the optimization with the electrons distributed randomly on a sphere of radius 1/2 centered at [0,0,–1].

rng default % For reproducibility
x0 = randn(N,3);
for ii=1:N
    x0(ii,:) = x0(ii,:)/norm(x0(ii,:))/2;
    x0(ii,3) = x0(ii,3) - 1;
end
init.x = x0(:,1);
init.y = x0(:,2);
init.z = x0(:,3);

Solve the problem by calling solve.

[sol,fval,exitflag,output] = solve(elecprob,init)
Local 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.
sol = struct with fields:
    x: [10x1 double]
    y: [10x1 double]
    z: [10x1 double]

fval = 34.1365
exitflag = 
    OptimalSolution

output = struct with fields:
         iterations: 95
          funcCount: 2990
    constrviolation: 0
           stepsize: 1.8867e-06
          algorithm: 'interior-point'
      firstorderopt: 1.9841e-06
       cgiterations: 0
            message: '...'
             solver: 'fmincon'

View Solution

Plot the solution as points on the conducting body.

figure(hand)
plot3(sol.x,sol.y,sol.z,'r.','MarkerSize',25)
hold off

The electrons are distributed fairly evenly on the constraint boundary. The electrons are mainly on edges and the pyramid point.

Reference

[1] Dolan, Elizabeth D., Jorge J. Moré, and Todd S. Munson. “Benchmarking Optimization Software with COPS 3.0.” Argonne National Laboratory Technical Report ANL/MCS-TM-273, February 2004.

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