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    • 1. 发明授权
    • Method of efficient gradient computation
    • 高效梯度计算方法
    • US5886908A
    • 1999-03-23
    • US825278
    • 1997-03-27
    • Andrew Roger ConnRudolf Adriaan HaringChandramouli Visweswariah
    • Andrew Roger ConnRudolf Adriaan HaringChandramouli Visweswariah
    • G06F17/50G06F9/455
    • G06F17/5036G06F17/5063
    • A method of efficient computation of gradients of a merit function of a system includes the steps of: specifying at least one parameter for which the gradients with respect to the at least one parameter are desired; specifying the merit function of interest in terms of observable measurements of the system; either solving or simulating the system to determine values of the measurements; expressing the gradients of the merit function as the gradient of a weighted sum of measurements; forming an appropriately configured adjoint system; and either solving or simulating the adjoint system to simultaneously determine the gradients of the merit function with respect to the at least one parameter by employing a single adjoint analysis. Preferably, the system may be modeled by a set of equations comprising at least one of the following: a nonlinear set of equations, a linear set of equations, a set of linear partial differential equations, a set of nonlinear partial differential equations, a set of linear differential algebraic equations or a set of nonlinear differential algebraic equations. Further, the system of interest may be a network and, preferably, may be an electrical circuit. Still further, elements of the adjoint network and excitations of the adjoint network are determined in order to obtain the gradients of the merit function by employing a single adjoint analysis. It is to be appreciated that, in a preferred embodiment, the gradients of merit function are computed for the purpose of optimization and the merit function may be either a Lagrangian merit function or an augmented Lagrangian merit function.
    • 一种有效计算系统的优值函数的梯度的方法包括以下步骤:指定至少一个参数,该参数相对于至少一个参数的梯度是期望的; 根据系统的可观测量指定感兴趣的优点功能; 解决或模拟系统以确定测量值; 将优值函数的梯度表示为加权求和和的梯度; 形成适当配置的伴随系统; 并且通过采用单一伴随分析来解决或模拟伴随系统以同时确定关于至少一个参数的优值函数的梯度。 优选地,可以通过包括以下至少一个的一组方程来建模系统:非线性方程组,线性方程组,一组线性偏微分方程,一组非线性偏微分方程,一组 的线性微分代数方程或一组非线性微分代数方程。 此外,感兴趣的系统可以是网络,并且优选地可以是电路。 此外,确定伴随网络的元素和伴随网络的激励,以便通过采用单个伴随分析来获得优值函数的梯度。 应当理解,在优选实施例中,为优化目的而计算优值函数的梯度,并且优值函数可以是拉格朗日优值函数或增强的拉格朗日优值函数。
    • 2. 发明授权
    • Method for incorporating noise considerations in automatic circuit
optimization
    • 将噪声考虑纳入自动电路优化的方法
    • US5999714A
    • 1999-12-07
    • US56430
    • 1998-04-07
    • Andrew Roger ConnRudolf Adriaan HaringChandramouli Visweswariah
    • Andrew Roger ConnRudolf Adriaan HaringChandramouli Visweswariah
    • G06F17/50G06F9/455
    • G06F17/5036G06F17/5063
    • A method of incorporating noise considerations during circuit optimization includes the steps of: specifying a circuit schematic to be optimized; specifying at least one noise criterion as a noise measurement, including the signal to be checked for noise, the sub-interval of time of interest, and the maximum allowable noise deviation; providing each noise criterion as either a semi-infinite constraint or a semi-infinite objective function; specifying at least one variable of the optimization; converting the semi-infinite noise constraints and the semi-infinite noise objective functions into time-integral equality constraints; optionally, if required, providing additional optimization criteria other than noise as, for each such criterion, either objective functions or constraints; creating a merit function to be minimized to solve the optimization problem; simulating the circuit in the time-domain; computing the values of the objective functions and constraints; efficiently computing the gradients of the merit function of the optimizer (including contributions of all objective functions and constraints and the time-integrals representing noise considerations) preferably by means of a single adjoint analysis; iteratively providing the constraint values, the objective function values and the gradients of the merit function to a nonlinear optimizer; and continuing the optimization iterations to convergence.
    • 在电路优化期间引入噪声考虑的方法包括以下步骤:指定要优化的电路原理图; 将至少一个噪声标准指定为噪声测量,包括要检查噪声的信号,感兴趣的时间的次间隔和最大允许噪声偏差; 将每个噪声标准提供为半无限约束或半无限目标函数; 指定优化的至少一个变量; 将半无限噪声约束和半无限噪声目标函数转换为时间积分等式约束; 可选地,如果需要,提供除噪声之外的附加优化标准,对于每个这样的标准,目标函数或约束; 创建优化函数以最小化以解决优化问题; 在时域模拟电路; 计算目标函数和约束的值; 优选地通过单个伴随分析来优化优化器的优值函数的梯度(包括所有目标函数和约束的贡献以及表示噪声考虑的时间积分); 将优化函数的约束值,目标函数值和梯度迭代地提供给非线性优化器; 并继续优化迭代来收敛。