In mathematics, there are two different notions of semi-inner-product. The first, and more common, is that of an inner product which is not required to be strictly positive. This article will deal with the second, which is an inner product not required to be conjugate symmetric. It was formulated by Günter Lumer, for the purpose of extending Hilbert space type arguments to Banach spaces in functional analysis.[1] Fundamental properties were later explored by Giles.[2]


We mention again that the definition presented here is different from that of the "semi-inner product" in standard functional analysis textbooks,[3] where a "semi-inner product" satisfies all the properties of inner products (including conjugate symmetry) except that it is not required to be strictly positive.

A semi-inner-product for a linear vector space   over the field   of complex numbers is a function from   to  , usually denoted by  , such that

  1.  ,

Difference from inner productsEdit

A semi-inner-product is different from inner products in that it is in general not conjugate symmetric, i.e.,


generally. This is equivalent to saying that [4]


In other words, semi-inner-products are generally nonlinear about its second variable.

Semi-inner-products for Banach spacesEdit

  • If   is a semi-inner-product for a linear vector space   then

defines a norm on  .

  • Conversely, if   is a normed vector space with the norm   then there always exists a (not necessarily unique) semi-inner-product on   that is consistent with the norm on   in the sense that


  • The Euclidean space   with the   norm ( )

has the consistent semi-inner-product:



  • In general, the space   of  -integrable functions on a measure space  , where  , with the norm

possesses the consistent semi-inner-product:



  1. Following the idea of Lumer, semi-inner-products were widely applied to study bounded linear operators on Banach spaces.[5][6][7]
  2. In 2007, Der and Lee applied semi-inner-products to develop large margin classification in Banach spaces.[8]
  3. Recently, semi-inner-products have been used as the main tool in establishing the concept of reproducing kernel Banach spaces for machine learning.[9]
  4. Semi-inner-products can also be used to establish the theory of frames, Riesz bases for Banach spaces.[10]


  1. ^ Lumer, G. (1961), "Semi-inner-product spaces", Transactions of the American Mathematical Society, 100: 29–43, doi:10.2307/1993352, MR 0133024.
  2. ^ J. R. Giles, Classes of semi-inner-product spaces, Transactions of the American Mathematical Society 129 (1967), 436–446.
  3. ^ J. B. Conway. A Course in Functional Analysis. 2nd Edition, Springer-Verlag, New York, 1990, page 1.
  4. ^ S. V. Phadke and N. K. Thakare, When an s.i.p. space is a Hilbert space?, The Mathematics Student 42 (1974), 193–194.
  5. ^ S. Dragomir, Semi-inner Products and Applications, Nova Science Publishers, Hauppauge, New York, 2004.
  6. ^ D. O. Koehler, A note on some operator theory in certain semi-inner-product spaces, Proceedings of the American Mathematical Society 30 (1971), 363–366.
  7. ^ E. Torrance, Strictly convex spaces via semi-inner-product space orthogonality, Proceedings of the American Mathematical Society 26 (1970), 108–110.
  8. ^ R. Der and D. Lee, Large-margin classification in Banach spaces, JMLR Workshop and Conference Proceedings 2: AISTATS (2007), 91–98.
  9. ^ Haizhang Zhang, Yuesheng Xu and Jun Zhang, Reproducing kernel Banach spaces for machine learning, Journal of Machine Learning Research 10 (2009), 2741–2775.
  10. ^ Haizhang Zhang and Jun Zhang, Frames, Riesz bases, and sampling expansions in Banach spaces via semi-inner products, Applied and Computational Harmonic Analysis 31 (1) (2011), 1–25.