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greatly exaggerated 55 percent of students in 1932 supported Herbert Hoover over Franklin Roosevelt with many student leaders presenting evidence in corroboration50 For his part, Hutchins played the dicey situation like a pro, first defending the importance of academic freedom in order to soothe his professors, then providing reassurances to outsiders like Walgreen A member of the faculty, Hutchins said, must be allowed to join any church, club, or party, and think, live, worship, and vote as he pleases But he added that the University would dismiss any professor who, before an appropriate tribunal, was proved to have advocated the overthrow of the government by violence 51 The controversy had been settled to everyone s apparent satisfaction, but Hutchins wisely didn t stop there To court Walgreen and to convert him to the university s cause, he enlisted the help of William Benton, a wealthy Yale graduate who had recently retired from his advertising firm Benton established a friendship with Walgreen over several months Knowing of his passionate belief in the American system, Benton asked Walgreen, Why don t you give money to the university to help the university correct the defect which you feel is in it Walgreen replied that he would be glad to consider this He felt some remorse that the imbroglio might limit donations to the university and, perhaps, that his concerns might have been fanned by Lucille s partially inaccurate assessment52 Hutchins took it from there, enjoying long conversations with Walgreen at the company headquarters at Bowen Avenue and at the school s Quadrangle Club He also encouraged the CEO to visit any lecture and classroom he wished, to see for himself the university s mission53 If Charles Walgreen was a master at converting upset customers to loyal patrons, President Hutchins was his equal Hutchins s efforts soon bore fruit On June 5, 1937 over two years after Walgreen first wrote Hutchins President Hutchins announced the creation of the Charles R Walgreen Foundation for the Study of American Institutions to forward the development of good citizenship and the improvement of public service with a gift of $550,000 in.

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A Lagrange-multiplier approach for probabilistic constraints A heuristic approach for handling probabilistic constraints is the Lagrange-multiplier method This method produces only stationary non-randomized policies To describe the method, assume a single probabilistic constraint ia fia ( )

i I a A(i)

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on the state-action frequencies In the Lagrange-multiplier method, the constraint is eliminated by putting it into the criterion function by means of a Lagrange multiplier 0 That is, the goal function is changed from i,a ci (a)xia to ci (a)xia + ( i,a ia xia ) The Lagrange multiplier may be interpreted as i,a the cost to each unit that is used from some resource The original Markov decision problem without probabilistic constraint is obtained by taking = 0 It is assumed that the probabilistic constraint is not satis ed for the optimal stationary policy in the unconstrained problem; otherwise, this policy is optimal for the constrained problem as well Thus, for a given value of the Lagrange multiplier > 0, we consider the unconstrained Markov decision problem with one-step costs

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Transparently transferred ISUP messages (examples) Description An event has occurred during call setup which should be relayed to the calling party CPG may contain charge indicator, called party s status indicator, etc FAC may be used to inform the calling party about pending call transfer RES indicates that the call is no longer on hold SUS indicates that the call is temporarily placed on hold UUS information may be used during call set up and during active call, to transfer information between calling and called party

and one-step transition probabilities pij (a) as before Solving this unconstrained Markov decision problem yields an optimal deterministic policy R( ) that prescribes always a xed action Ri ( ) whenever the system is in state i Let ( ) be the constraint level associated with policy R( ), that is, ( ) =

i,Ri ( ) fi,Ri ( ) (R( ))

If ( ) > one should increase , otherwise one should decrease Why The Lagrange multiplier should be adjusted until the smallest value of is found for which ( ) Bisection is a convenient method to adjust How do we calculate ( ) for a given value of To do so, observe that ( ) can be interpreted as the average cost in a single Markov chain with an appropriate cost structure Consider the Markov chain describing the state of the system under policy R( ) In this Markov process, the long-run average cost per time unit equals ( ) when it is assumed that a direct cost of i,Ri ( ) is incurred each time the process visits state i An effective method to compute the average cost ( ) is to apply value iteration to a single Markov chain; see Example 661 in the next section The average cost of the stationary policy obtained by the Lagrangian approach will in general be larger than the average cost of the stationary randomized policy resulting from the linear programming formulation Also, it should be pointed out that there is no guarantee that the policy obtained by the Lagrangian approach is the best policy among all stationary policies satisfying the probabilistic constraint, although in most practical situations this may be expected to be the case In spite

of the possible pitfalls of the Lagrangian approach, this approach may be quite useful in practical applications having a speci c structure

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