Gradient descent with variable algorithm

To solve this first reduced problem, follow the steps of the descent algorithm outlined at the start of this section with some straightforward modifications that account for the bounds on x and y. When a nonbasic variable is at a bound, we must decide whether it should be allowed to leave the bound or be forced to remain at that bound for the next iteration. Those nonbasic variables that will not be kept at their bounds are called superbasic variables [this term was coined by Murtaugh and Saunders (1982)]. In step 1 the reduced gradient off(x,y) is... 

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