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When the AMT clearly will be paid this year without changes, accelerate income into this year Bringing extra income into one year could increase the regular income tax and might avoid the AMT If that happens, tax breaks won t be lost under the AMT The other possible effect is that, if the AMT cannot be avoided, a maximum marginal tax rate of 28 percent on the additional income is assured, instead of the usual maximum 35 percent rate There is a potential trap to this strategy The AMT exemption amount is phased out at higher incomes The phaseout occurs between $150,000 and $346,000 on joint returns ($112,400 and $255,500 on single returns) Within the phaseout range, 25 cents of the exemption is lost for each additional dollar of income.

That converts the 28 percent AMT rate into a marginal tax rate of 35 percent on each additional dollar of income A taxpayer should not accelerate income if it will put income into this phaseout range Plan state and local tax payments Most taxpayers can control when at least some of their state and local taxes are paid For example, the final payment of a year s estimated income taxes might not be due until the following January The check, however, can be written in December of the tax year if the taxpayer chooses Similar payment flexibility often is available with real estate and personal property taxes This flexibility can be used to move state tax payments into the year that provides the most tax benefits Time capital gains.

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TABLE 4.1. Differences in simulation resulting from an incomplete sensitivity list Complete Sensitivity List module sense (inputA, inputB, inputC, outputA); input inputA, inputB, inputC; output outputA; reg outputA; always @ (inputA or inputB or inputC) begin if (inputA & inputB & inputC) outputA = 0; else outputA = 1; end endmodule Incomplete Sensitivity List module sense (inputA, inputB, inputC, outputA); input inputA, inputB, inputC; output outputA; reg outputA; always @ (inputA or inputB) begin if (inputA & inputB & inputC) outputA = 0; else outputA = 1; end endmodule Testbench to Simulate Both the Verilog Code module sense_tb(); reg reg_inputA, reg_inputB, reg_inputC; wire wire_outputA; integer i; initial begin for (i = 0; i < 8; i = i + 1) begin {reg_inputA, reg_inputB, reg_inputC} = i; #100; end end sense sense_inst (.inputA(reg_inputA), .inputB(reg_inputB), .inputC(reg_inputC), .outputA(wire_outputA)); initial begin $monitor ( reg_inputA %b reg_inputB %b reg_inputC %b wire_outputA %b\n , reg_inputA, reg_inputB, reg_inputC, wire_outputA); end endmodule

enc(trj ) then Exitj ; actsj ; Entryj )

The 15 percent long-term capital gains tax rate can trigger the AMT in a year when the taxpayer has a lot of capital gains The large gains could cause the loss of deductions and increase the effective tax rate on the capital gains Be careful about taking a lot of capital gains in one year and avoid large investments in mutual funds that have histories of making significant capital gains distributions Reevaluate tax planning for the year if a mutual fund declares a large capital gain distribution As year end draws near, try to get estimates of capital gain distributions from mutual funds and be prepared to readjust your tax plan Examine exempt bonds and bond funds Yields from private activity municipal bonds are higher than from regular exempt bonds, partly because the higher yield is taxable under the AMT.

Another technique for the exact solution of closed product-form queueing networks is the RECAL (recursion by chain algorithm) algorithm. RECAL is very well suited for networks with a large number of job classes but a small number of nodes. For the explanation of this method we assume that we have a product-form network N consisting only of single server or infinite server nodes. We consider only the case of a multiple class network (R > 1) and introduce an extended normalizing constant GR(v) for the network with the Here, GR (0) is the standard normalizing constant vector v = (wi,...,~~). G(K), called G for short in the following. In [CoGe86] a recursive expression for computing G = GR (0) is given, which relates the normalizing constant of a network with r classes and the normalizing constants of a set of networks with each of the (r - 1) classes. The RECAL algorithm uses a simplified version of this general relation. This simplification can be given when each job class contains exactly one job, because if K, = 1 for r = 1,. . . , R, then the normalizing constant GR(O) is recursively given by the expression [CoGe86]: Gr(vr) with: = c(1

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