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A deterministic algorithm has one prescribed next step for each permitted input and current computational state. Thus the same input and initial state give the same execution trace and, if execution terminates, the same output. This is a statement about the abstract transition rules, not about identical elapsed time on different computers.

Binary search with a fixed midpoint rule is deterministic: every comparison determines which remaining interval is searched next. Quicksort with a fixed pivot-selection rule is also deterministic, even though different inputs can produce very different running times. Determinism therefore does not imply a constant or small time complexity.

A randomized algorithm additionally receives random choices. Fixing its entire random-choice sequence makes its subsequent execution deterministic, but averaging over those choices is essential to its randomized guarantees. A Nondeterministic Turing machine is a different mathematical model: its branches describe possible computation paths, rather than a probability distribution supplied by an implementation.

A polynomial-time Turing reduction from a decision problem $A$ to a decision problem $B$ is a deterministic algorithm deciding membership in $A$ in polynomial time when allowed to query whether strings belong to $B$. An oracle Turing machine models each query answer as a single oracle operation, while writing the query and using the answer still costs time. Queries may depend on earlier answers.

For example, the complement of $B$ reduces to $B$ by asking one membership question and reversing its answer. This is a Turing reduction, regardless of whether there is a simple many-one reduction with the same effect. A polynomial-time many-one reduction instead computes one transformed instance whose answer is used unchanged.

If $A$ reduces to $B$ this way and $B$ has a polynomial-time algorithm, then $A$ does too: substitute that algorithm at each oracle call. Polynomially many queries of polynomial length keep the total cost polynomial. Completeness claims must specify which reduction is intended because adaptive access is more permissive.

Valentine Kabanets is a computer scientist whose research concerns computational complexity theory, derandomization, pseudorandomness and circuit complexity. A recurring question in his research is when efficient randomized algorithms can be replaced by efficient deterministic algorithms.^[ref]

His research description emphasizes a connection between designing fast algorithms and proving circuit lower bounds. An upper bound supplies an efficient method for a problem; a lower bound rules out methods within a specified model. In some settings, progress on one side can imply progress on the other, turning apparent obstacles into tools.^[ref]

Identity source: www2.cs.sfu.ca/~kabanets/