Parameter Constraints in Nonlinear Model Fitting

[#1]

I am fitting a bi-exponential growth model of the following form:

I would like to constrain the parameter estimates so that dmax1< dmax2 and rate1<rate2.  

Is there any way to do this in the Nonlinear platform?  

[#2]

@Ben_BarrIngh , add-on question:
when I use "By" to fit multiple spectra, is there a trick to set global constraints and the lock?

something like:

new column ("by",Nominal, set each value(mod(row(),3)));
Nonlinear( Y( :input ), X( :fit ), by(:by),
SendToByGroup( Bygroup Default, 		
Parameter Bounds(
			h1( 0, 2 ),
			x1( 0, 100),
			h1( 0, 2 ),
			x2( 100, 200)
		) )
 )     

[#3]

workaround for the global constraints:

new column ("by",Nominal, set each value(mod(row(),3)));
nl = Nonlinear( Y( :input ), X( :fit ), by(:by) );

nl << Parameter Bounds(
			h1( 0, 2 ),
			x1( 0, 100 ),
			h2( 0, 2 ),
			x2( 100, 200 )
		)

The Lock doesn't seem to be recorded.
One could set constraints with min=max.

[#4]

I wonder if it would be easier to process the estimates after the nonlinear fitting without contstraints. I assume that the dmax1 and rate1 parameter estimates will go together and the dmax2 and rate2 parameter estimates will go together even if the dmax1 and rate1 parameter estimates are not always first. Then to achieve the order that you want, you can simply swap the order in the data table after saving the estimates table. This example shows what I have in mind:

Names Default to Here( 1 );

dt = New Table( "Double Exponential Models",
	Add Rows( 20 ),
	New Column( "dmax1",
		Values( J( 20, 1, Random Uniform() ) )
	),
	New Column( "rate1",
		Values( J( 20, 1, Random Uniform() ) )
	),
	New Column( "dmax2",
		Values( J( 20, 1, Random Uniform() ) )
	),
	New Column( "rate2",
		Values( J( 20, 1, Random Uniform() ) )
	)
);

Wait( 5 );

For Each Row(
	If( :rate1 > :rate2,
		temp = :dmax1;
		:dmax1 = :dmax2;
		:dmax2 = temp;
		temp = :rate1;
		:rate1 = :rate2;
		:rate2 = temp;
	);
);

[#5]

I had wrote some JSL that was nearly identical to that, and it did solve the problem.  Thanks for sharing.