A trust-region interior-point technique to solve multi-objective optimization problems and its application to a tuberculosis optimal control problem
Bibliographic record
Abstract
We introduce a trust-region interior-point technique to generate the Pareto optimal solution for multi-objective optimization problems. The Pascoletti-Serafini scalarization technique is utilized to convert a multi-objective optimization problem into a set of single-objective optimization subproblems. Then, the subproblems are solved by a trust-region interior-point method. Using the sequential quadratic programming technique, the algorithm proceeds through a sequence of barrier problems. With the help of the stationary points of a merit function, we obtain stationary points of the objective function of the barrier problem. It is shown that the directions that are used to find the sequence of iterates of the proposed method are descent direction of the used merit function. To show the efficiency of the proposed method, we show its performance on some standard test problems. As an application, we apply the proposed algorithm to solve an optimal control problem for a tuberculosis model. The model problem is a minimization problem and it has two objectives: one is the sum of the active infections patient and persistent latent individual, and the other is the cost to implement the control strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".