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1-D interval location using Brent’s method


[a,gX,perf,retcode,delta,tol] = srchbre(net,X,Pd,Tl,Ai,Q,TS,dX,gX,perf,dperf,delta,tol,ch_perf)


srchbre is a linear search routine. It searches in a given direction to locate the minimum of the performance function in that direction. It uses a technique called Brent’s technique.

[a,gX,perf,retcode,delta,tol] = srchbre(net,X,Pd,Tl,Ai,Q,TS,dX,gX,perf,dperf,delta,tol,ch_perf) takes these inputs,


Neural network


Vector containing current values of weights and biases


Delayed input vectors


Layer target vectors


Initial input delay conditions


Batch size


Time steps


Search direction vector


Gradient vector


Performance value at current X


Slope of performance value at current X in direction of dX


Initial step size


Tolerance on search


Change in performance on previous step

and returns


Step size that minimizes performance


Gradient at new minimum point


Performance value at new minimum point


Return code that has three elements. The first two elements correspond to the number of function evaluations in the two stages of the search. The third element is a return code. These have different meanings for different search algorithms. Some might not be used in this function.

 0  Normal
 1  Minimum step taken
 2  Maximum step taken
 3  Beta condition not met

New initial step size, based on the current step size


New tolerance on search

Parameters used for the Brent algorithm are


Scale factor that determines sufficient reduction in perf


Scale factor that determines sufficiently large step size


Largest step size


Parameter that relates the tolerance tol to the initial step size delta, usually set to 20

The defaults for these parameters are set in the training function that calls them. See traincgf, traincgb, traincgp, trainbfg, and trainoss.

Dimensions for these variables are


No-by-Ni-by-TS cell array

Each element P{i,j,ts} is a Dij-by-Q matrix.


Nl-by-TS cell array

Each element P{i,ts} is a Vi-by-Q matrix.


Nl-by-LD cell array

Each element Ai{i,k} is an Si-by-Q matrix.


Ni =net.numInputs
Nl =net.numLayers
LD =net.numLayerDelays
Ri =net.inputs{i}.size
Vi= net.targets{i}.size
Dij=Ri * length(net.inputWeights{i,j}.delays)


Here is a problem consisting of inputs p and targets t to be solved with a network.

p = [0 1 2 3 4 5];
t = [0 0 0 1 1 1];

A two-layer feed-forward network is created. The network’s input ranges from [0 to 10]. The first layer has two tansig neurons, and the second layer has one logsig neuron. The traincgf network training function and the srchbac search function are to be used.

Create and Test a Network

net = newff([0 5],[2 1],{'tansig','logsig'},'traincgf');
a = sim(net,p)

Train and Retest the Network

net.trainParam.searchFcn = 'srchbre';
net.trainParam.epochs = 50; = 10;
net.trainParam.goal = 0.1;
net = train(net,p,t);
a = sim(net,p)

Network Use

You can create a standard network that uses srchbre with newff, newcf, or newelm. To prepare a custom network to be trained with traincgf, using the line search function srchbre,

  1. Set net.trainFcn to 'traincgf'. This sets net.trainParam to traincgf’s default parameters.

  2. Set net.trainParam.searchFcn to 'srchbre'.

The srchbre function can be used with any of the following training functions: traincgf, traincgb, traincgp, trainbfg, trainoss.

More About

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Brent’s Search

Brent’s search is a linear search that is a hybrid of the golden section search and a quadratic interpolation. Function comparison methods, like the golden section search, have a first-order rate of convergence, while polynomial interpolation methods have an asymptotic rate that is faster than superlinear. On the other hand, the rate of convergence for the golden section search starts when the algorithm is initialized, whereas the asymptotic behavior for the polynomial interpolation methods can take many iterations to become apparent. Brent’s search attempts to combine the best features of both approaches.

For Brent’s search, you begin with the same interval of uncertainty used with the golden section search, but some additional points are computed. A quadratic function is then fitted to these points and the minimum of the quadratic function is computed. If this minimum is within the appropriate interval of uncertainty, it is used in the next stage of the search and a new quadratic approximation is performed. If the minimum falls outside the known interval of uncertainty, then a step of the golden section search is performed.

See [Bren73] for a complete description of this algorithm. This algorithm has the advantage that it does not require computation of the derivative. The derivative computation requires a backpropagation through the network, which involves more computation than a forward pass. However, the algorithm can require more performance evaluations than algorithms that use derivative information.


srchbre brackets the minimum of the performance function in the search direction dX, using Brent’s algorithm, described on page 46 of Scales (see reference below). It is a hybrid algorithm based on the golden section search and the quadratic approximation.


Scales, L.E., Introduction to Non-Linear Optimization, New York, Springer-Verlag, 1985

Introduced before R2006a

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