Results

**11 - 14**of**14**### Time in Yrs Annual Coupon Market Price

"... The ordinary bootstrap method for computing forward rates from zero rates generates posynomial equations as introduced in an area of optimization termed geometric programming invented by Duffin, Peterson, and Zener [6]. posynomial disc. fns e−zk(tk−t0) �k−1 = i=0 x (ti+1−ti) i,i+1, k = 1,... express ..."

Abstract
- Add to MetaCart

The ordinary bootstrap method for computing forward rates from zero rates generates posynomial equations as introduced in an area of optimization termed geometric programming invented by Duffin, Peterson, and Zener [6]. posynomial disc. fns e−zk(tk−t0) �k−1 = i=0 x (ti+1−ti) i,i+1, k = 1,... express the forward rates zk(tk − t0) = � k−1 i=0 fi,i+1(ti+1 − ti), where xi,i+1 = e −fi,i+1 in Tables 2–4. Note that the are n equations in m unknowns (n = m =5). Ordinary bootstrapping does not work when n � = m, eg., if there were no 0.5 time T–Bill. 1 (1)

### An Algorithm for Posynomial Geometric Programming, Based on Generalized Linear Programming

, 1995

"... This paper describes a column generation algorithm for posynomial geometric programming that ..."

Abstract
- Add to MetaCart

This paper describes a column generation algorithm for posynomial geometric programming that

### Artificial Immune System for Solving Generalized Geometric Problems: A Preliminary Results

"... Generalized geometric programming (GGP) is an optimization method in which the objective function and constraints are nonconvex functions. Thus, a GGP problem includes multiple local optima in its solution space. When using conventional nonlinear programming methods to solve a GGP problem, local opt ..."

Abstract
- Add to MetaCart

Generalized geometric programming (GGP) is an optimization method in which the objective function and constraints are nonconvex functions. Thus, a GGP problem includes multiple local optima in its solution space. When using conventional nonlinear programming methods to solve a GGP problem, local optimum may be found, or the procedure may be mathematically tedious. To find the global optimum of a GGP problem, a bioimmune-based approach is considered. This study presents an artificial immune system (AIS) including: an operator to control the number of antigen-specific antibodies based on an idiotypic network hypothesis; an editing operator of receptor with a Cauchy distributed random number, and a bone marrow operator used to generate diverse antibodies. The AIS method was tested with a set of published GGP problems, and their solutions were compared with the known global GGP solutions. The testing results show that the proposed approach potentially converges to the global solutions.

### GLOBERSON & JAAKKOLA 133 Convergent Propagation Algorithms via Oriented Trees

"... Inference problems in graphical models are often approximated by casting them as constrained optimization problems. Message passing algorithms, such as belief propagation, have previously been suggested as methods for solving these optimization problems. However, there are few convergence guarantees ..."

Abstract
- Add to MetaCart

Inference problems in graphical models are often approximated by casting them as constrained optimization problems. Message passing algorithms, such as belief propagation, have previously been suggested as methods for solving these optimization problems. However, there are few convergence guarantees for such algorithms, and the algorithms are therefore not guaranteed to solve the corresponding optimization problem. Here we present an oriented tree decomposition algorithm that is guaranteed to converge to the global optimum of the Tree-Reweighted (TRW) variational problem. Our algorithm performs local updates in the convex dual of the TRW problem – an unconstrained generalized geometric program. Primal updates, also local, correspond to oriented reparametrization operations that leave the distribution intact. 1