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Elementary Gradient-Based Optimizers: CSLS and ISD

Excerpt

Regardless of the DV dimension, the negative gradient indicates the direction of steepest decent, which is a logical direction to begin the search. This chapter presents two gradient-based searches: Cauchy’s sequential line search (CSLS) and incremental steepest descent (ISD). Although the exercises will primarily be in 2-D applications, these are applicable to N-D situations.

Regardless of the DV dimension, the negative gradient indicates the direction of steepest decent, which is a logical direction to begin the search. This chapter presents two gradient-based searches: Cauchy’s sequential line search (CSLS) and incremental steepest descent (ISD). Although the exercises will primarily be in 2-D applications, these are applicable to N-D situations.

8.1Introduction
8.2Cauchy’s Sequential Line Search
8.3Incremental Steepest Descent
8.4Takeaway
8.5Exercises
8.1Introduction
8.2Cauchy’s Sequential Line Search
8.3Incremental Steepest Descent
8.4Takeaway
8.5Exercises
Topics: Dimensions

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