During optimization, I have a problem that gets to a point where the 
gradient looks like this,

[0] gradient = t( matrix(c(    # 51x1
 -31780.195562, -38508.674735, -50973.208738, -55408.084812, 
-66931.026056, -74286.656477, -80710.037658, -32059.100573, 
-40421.260358, -47351.363022, -56331.397546, -67570.244335, 
-71730.720066, -80617.938563, -30100.959330, -40235.309256, 
-47047.853982, -56263.225828, -67009.403836, -75987.083372, 
-84897.553874, -62848.356157, -210680.805240, -214470.061730, 
-234536.487353, -241645.958717, -262177.928304, -278085.836093, 
-286557.250051, -63655.124325, -214492.388535, -219036.315591, 
-224526.862707, -240634.971741, -248375.386046, -263267.566427, 
-289505.767031, -64899.664139, -213624.942908, -222084.527486, 
-223847.520561, -236782.323103, -258931.376366, -276562.312245, 
-304241.522192, -25035.332609, -25125.410641, -25003.755675, 
-4302.824452, -4986.836691, -3574.837605), byrow=TRUE, nrow=1, ncol=51))

This point is nowhere near the minimum. Is it significant that all the 
gradients are negative? Is that why SLSQP cannot determine a search 
direction? When this occurs, could SLSQP use the gradient as the search 
direction?

-- 
Joshua N. Pritikin
Department of Psychology
University of Virginia
485 McCormick Rd, Gilmer Hall Room 102
Charlottesville, VA 22904
http://people.virginia.edu/~jnp3bc

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