Convexity and global gradient bounds
A differentiable function on an open convex domain is convex exactly when f(y)≥f(x)+∇f(x)·(y−x) for all domain points.
Key knowledge
A differentiable function on an open convex domain is convex exactly when f(y)≥f(x)+∇f(x)·(y−x) for all domain points. Thus a zero gradient certifies a global minimum; convex functions may have several minimizers.
Conditions and scope
- The domain must be convex and differentiability holds throughout it.
- Convexity does not by itself guarantee that a minimum is attained.
Sources · 1
References
Sources retain their recorded scope. A source link alone does not establish verification of every statement.
Evidence and review
Convex functions slide 3–7: first-order supporting-gradient characterization.
2026-10-10 · Independent AI editorial review: All241 compact claims, hypotheses, displayed equations/conventions and all596 origin semantic links read; entire241-entry author exact-source inspection matrix and declared limitations reviewed. Prior105 actual inherited Mathematics/Information records read for whole-inventory breadth. Targeted independent primary sections are listed below; this does NOT claim every cited source body or every proof was independently re-read. This is AI reference-content review, not human disciplinary certification, exhaustive textbook reproduction or experimental replication. Receipt: mathematics-information-independent-content-review.1.
Entry details
Permanent link to this entry ↗- Record identity
- math-convex-gradient-condition
- Revision
- university-core.1
- Structured reference record
- University core expansion