Minimax Design of IIR Digital Filters Using Iterative SOCP
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Bibliographic record
Abstract
In this paper, a novel method for IIR digital filter design using iterative second-order cone programming (SOCP) is proposed under the minimax criterion. The convex relaxation technique is utilized to transform the original nonconvex design problem into an SOCP problem. By solving the relaxed problem, the lower and upper bounds on the optimal value of the original problem can be obtained. In order to reduce the discrepancy between the original and relaxed design problems, an iterative procedure is developed. At each iteration, a linear constraint is further incorporated to guarantee the convergence of the iterative procedure. In practice, the convergence speed can be further improved by introducing a soft threshold variable in this linear constraint. Accordingly, a regularization term is incorporated in the objective function of the design problem at each iteration. The stability of the designed filters can be ensured by a new positive realness based linear constraint. Several examples are presented to demonstrate the effectiveness of the proposed method.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it