What question did this study set out to answer?

To develop a compact and reliable implementation of the Marquardt algorithm for minimizing non-linear sum of squares functions within the memory limitations of small computers.

January 1, 1977IMA Journal of Applied Mathematics

Minimizing a Non-linear Sum of Squares Function on a Small Computer

View Full Paper

Ask AI

Bookmark

Authors

JNJohn C. NashRutgers, The State University of New Jersey

Discussion

Member takes

Implication

Computational study demonstrates reliable non-linear sum of squares minimization on small computers, indicating that careful programming avoids algorithmic complexity.

Key Points

To develop a compact and reliable implementation of the Marquardt algorithm for minimizing non-linear sum of squares functions within the memory limitations of small computers.
Designed a streamlined implementation of the Marquardt algorithm optimized for low memory usage on small computing systems.
Evaluated the compact implementation against difficult, pathological functions previously reported to cause algorithmic failure.
The compact Marquardt implementation reliably converges on pathological functions without exhibiting the failures described in prior literature.
Demonstrates that meticulous program design overcomes performance issues without requiring more complex optimization algorithms.

Cite This Study

John C. Nash (1977) studied this question.

synapsesocial.com/papers/6a93fdb4b6c2e2f51e00dd2a https://doi.org/10.1093/imamat/19.2.231

View Full Paper

Ask AI

Bookmark

Implication

Computational study demonstrates reliable non-linear sum of squares minimization on small computers, indicating that careful programming avoids algorithmic complexity.

Minimizing a Non-linear Sum of Squares Function on a Small Computer

Discussion

Member takes

Implication

Key Points

Cite This Study

Minimizing a Non-linear Sum of Squares Function on a Small Computer

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider