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The least-squares solution to such problems is to minimize the [[L2 norm|<math>L^2</math> norm]]—that is, minimize the amount of energy in the system. This is usually simple mathematically (involving only a [[matrix multiplication]] by the [[pseudo-inverse]] of the basis sampled in). However, this leads to poor results for many practical applications, for which the unknown coefficients have nonzero energy.

The least-squares solution to such problems is to minimize the [[L2 norm|<math>L^2</math> norm]]—that is, minimize the amount of energy in the system. This is usually simple mathematically (involving only a [[matrix multiplication]] by the [[pseudo-inverse]] of the basis sampled in). However, this leads to poor results for many practical applications, for which the unknown coefficients have nonzero energy.

解决这类问题的最小二乘方法是使数学上的 L^2 / 数学规范最小化，即使系统中的能量最小化。这通常在数学上是简单的(只涉及矩阵乘以所采样基的伪逆)。然而，这导致许多实际应用的结果很差，因为未知系数具有非零能量。

解决这类问题的最小二乘方法是使数学上的 [[L2 norm|<math>L^2</math> norm]]最小化，即使系统中的能量最小化。这通常在数学上是简单的(只涉及矩阵乘以所采样基的伪逆)。然而，这导致许多实际应用的结果很差，因为未知系数具有非零能量。